Import Datasets
Candidate Genes
[Approach 1: Unsupervised Clustering]
- PCA: All Genes
- Unsupervised Clustering
[Approach 2: Compare Expression Profiles]
- Differential Expression
[Approach 3: Compare Network Modules]
- WGCNA: Macaque Microarray (SGZ/GCL samples only, all ages)
  - Rough ‘% overlap with genes in Tan module’ metric
- WGCNA: Macaque Microarray (all regions, all genes, all ages)
[Approach 4: Deconvolution]

PROJECT GOAL
Utilize brain-wide gene expression databases to identify regions that contain molecular signatures of adult neurogenesis.
DATASETS

ABA Mouse ISH
ABA Macaque microarray adult/development
ABA Human microarray adult/development
ABA Human RNA-seq adult/development
sc-RNAs-seq reference datasets for deconvolution:
1. Nathan Skene’s mouse meta-data
2. Neurogenic cells (Shin et al. 2015)
3. Human data (Darmanis et al. 2015)

Jeremy Miller Ideas:

Using Table S1 (or some other curated list), search the ABA for “neurons in waiting.”
Extend the analysis to include human brain. I’m not sure if the gene list from figure 5 in the paper you attached could be used directly (since the samples we collected from human were whole dentate gyrus, if I am remembering correctly), but that would make a good starting point.
A similar option would be to do a deconvolution analysis on RNA-Seq data from the BrainSpan atlas using the overlapping mouse/NHP[/human?] neurogenesis list as marker genes for SGZ. In theory we could show the relative decrease in SGZ neurons over time and also show that it doesn’t reach 0 (as long as we get any reads from these genes in the oldest donors).

Additional Ideas:

Use expression values for all genes in the SGZ (mouse or macaque) or sc-RNA-seq from neural stem cell, and compare to transcriptome expression from each brain region in an ABA atlas.

Very rough. Characterizes SGZ as a whole, not necessarilly what makes it neurogenic.

Look for similar logFC of gene sets for each ‘likelyCellType’ (Miller et al. 2013) (e.g. immature neuron, dividing cell) in one of the above atlases (after differential expression).

Can compare directly within respective species’ ABA atlases
Becomes interspecies when comparing to ABA human (likely diverged)

Using mouse or macaque modules (Miller et al. 2013) (e.g. tan), identify similar modules (based on similar kME values, or gene list overlap) one of the above atlases (after WGNCA) and then find which brain structures express them the most.

Problem: tan module was found by only analyzing 24 SGZ/GCL samples. Unlikely to recover the same tan module with full ABA macaque dataset.

Use sc-RNA-seq gene list to find similar patterns of expression across any of the ABA datasets.
Deconvolution:

EWCE: Using a sc-RNA-seq reference dataset and an ABA atlas.
Other methods: Use a gene list as a reference and ____ ____

Import Datasets

library(pander)
setwd("~/Desktop/Research/Signatures_of_Neurogensis/")

ABA data: Mouse

# Mouse SGZ & GCL: GSE39697

ABA data: Adult Human Microarray

load("/Users/schilder/Desktop/Dissertation/Gene_Expression/Raw data/Microarray/Full_Human_Microarray/All_ABA_humans.mni.RData")
# Set up eset
  sampleInfo.hum <-subset(sampleInfo.hum_all, select= -c(mni_x,mni_y,mni_z))

# Make all cols factors, & Reorder names alphabetically
  sampleInfo.hum <- lapply(sampleInfo.hum, factor)
  sampleInfo.hum <- sampleInfo.hum[order(names(sampleInfo.hum))]

library(annotate)
# Set up phenotype data
  pdata <- AnnotatedDataFrame(data.frame(sampleInfo.hum))
  sampleNames(pdata) <- sampleInfo.hum$unique_id
# Create ExpressionSet
  ABA_hum <- ExpressionSet(datExpr.hum_all, phenoData = pdata) #Correct this annot
# Add treatment condition
  ABA_hum$treatment <- "Human"

ABA data: Developmental Macaque Microarray

Macaque Preprocessing

library(WGCNA)
#mac_expression <- read.csv("Full Macaque Microarray/expression_matrix.csv", header=FALSE, row.names=1)
#mac_sampleinfo <- read.csv("Full Macaque Microarray/columns_metadata.csv", header=TRUE)
#mac_probes <- read.csv("Full Macaque Microarray/rows_metadata.csv", header=TRUE)
#save(mac_expression, mac_sampleinfo, mac_probes, file="Macaque Data.RData")
  # Saved as R objects to speed up importing data
load("~/Desktop/Dissertation/Gene_Expression/Raw data/Microarray/Macaque Data.RData")
datExpr <- mac_expression
probeInfo <- mac_probes
sampleInfo <- mac_sampleinfo
rm(mac_expression,mac_probes,mac_sampleinfo)
# Select only probes with associated entrez genes
  rownames(datExpr) <- probeInfo$probe_name
  validProbes = which(probeInfo$entrez_id>=0,arr.ind=TRUE)
  datExpr <- datExpr[validProbes,]
  probeInfo <- probeInfo[validProbes,]
# Some genes have more than one probe. The collapseRows function chooses a single probe to represent a gene.
probeInfo$gene_symbol <-gsub("[()]","",probeInfo$gene_symbol) # Gets rid of weird parentheses
  probeNames = probeInfo[which(probeInfo$probe_name == rownames(datExpr)),"gene_symbol"]
  probeIDs = rownames(datExpr)
  datCollapsed = WGCNA::collapseRows(datExpr, probeNames, probeIDs) # Collapse
  datExpr <- datCollapsed$datETcollapsed
  probeInfo <- probeInfo[which(datCollapsed$selectedRow==T, arr.ind=T),]
save(datExpr, probeInfo, sampleInfo,file="ABA.macaque_preprocessed.v4.RData")

Convert to eset

load("~/Desktop/Research/Signatures_of_Neurogensis/ABA.macaque_preprocessed.v4.RData")
datExpr.mac <-datExpr
probeInfo.mac <-probeInfo
sampleInfo.mac <-sampleInfo
rm(datExpr,probeInfo,sampleInfo)
library(annotate)
# Create eset
ex_mac <- ExpressionSet(datExpr.mac)
# Create macaque phenoData:
sampleInfo.mac$unique_id <- factor(paste(sampleInfo.mac$donor_name, sampleInfo.mac$structure_acronym, sampleInfo.mac$well_id, sep="_"))

p <- data.frame(sampleInfo.mac)
rownames(p) <- sampleInfo.mac$unique_id
pdata <- AnnotatedDataFrame(p)
# Replace phenoData in mac ExpressionSet
phenoData(ex_mac) <- pdata
# Insert condition
ex_mac$treatment <- "Macaque"

ex_mac <-ex_mac[,colSums(is.na(exprs(ex_mac))) == 0] # Remove cols with NA 

rm(datExpr.mac,probeInfo.mac,sampleInfo.mac,p,pdata)

Import Miller et al. 2013 gene sets

Miller J a, Nathanson J, Franjic D, Shim S, Dalley R a, Shapouri S, et al.: Conserved molecular signatures of neurogenesis in the hippocampal subgranular zone of rodents and primates. [Internet]. Development 2013;140:4633–44.

library(readxl)
# S1: Genes differentially expressed between mouse SGZ and GCL
S1 <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS1.xlsx",na = "NA"))
# Get rid of stars
S1$MouseGene <-gsub("[*]","",S1$MouseGene)

unique(S1$likelyCellType)

## [1] "Immature Neuron"           "Dividing Cell"            
## [3] "Red Blood Cell"            "Vascular Endothelial Cell"
## [5] "Oligodendrocyte"           "Astrocyte"                
## [7] "Hilar Interneuron"         "GABA Neuron"              
## [9] "(undetermined)"

  ## Get gene list
  immature.neuron <- S1[S1$likelyCellType=="Immature Neuron",]$MouseGene
  dividing.cell <- S1[S1$likelyCellType=="Dividing Cell",]$MouseGene
  S1.genes <- S1$MouseGene
  # Convert from mouse to human genes
  library(EWCE)
  data("mouse_to_human_homologs")
  m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
  s1.genes <- unique(m2h[m2h$MGI.symbol %in% S1.genes,"HGNC.symbol"])
  
  paste(sum(s1.genes %in% row.names(exprs(ex_mac))),"/",length(S1.genes),
          "'Mouse SGZ-enriched' genes found in ABA Macaque microarray dataset.")

## [1] "250 / 363 'Mouse SGZ-enriched' genes found in ABA Macaque microarray dataset."

# S3: Mouse network modules
mouse_modules <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS3.xlsx",na = "NA"))
  ## Subset specific modules
  ### tan module was associated with "neurogenesis" in Miller et al. 2013.
Mouse_mod.tan <- subset(mouse_modules, Module=="tan")$HumanOrtholog
  paste(sum(Mouse_mod.tan %in% row.names(exprs(ex_mac))),"/",length(Mouse_mod.tan),"Mouse Tan module genes found in ABA Macaque microarray dataset.")

## [1] "21 / 26 Mouse Tan module genes found in ABA Macaque microarray dataset."

# S4: Macaque network modules
macaque_modules <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS4.xlsx", na="NA"))
  ## Subset specific modules
  ### tan module was associated with "neurogenesis" in Miller et al. 2013.
  Mac_mod.tan <- subset(macaque_modules, Module=="tan")$Gene
  paste(sum(Mac_mod.tan %in% row.names(exprs(ex_mac))),"/",length( Mac_mod.tan),"Macaque Tan module genes found in ABA Macaque microarray dataset.")

## [1] "159 / 226 Macaque Tan module genes found in ABA Macaque microarray dataset."

Identify Macaque SGZ-enriched genes

# Perform DGE on Macaque SGZ vs. GCL
  SGZ.samples <-ex_mac[,pData(ex_mac)$structure_acronym==c("DGsg") ]
  GCL.samples <-ex_mac[,pData(ex_mac)$structure_acronym==c("DGgr") ]

# Limma
  library(limma); library(statmod)
  limma.data <-cbind(exprs(SGZ.samples), exprs(GCL.samples))
  fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(exprs(SGZ.samples))), rep(-1,ncol(exprs(GCL.samples)))) )
  fit <- eBayes(fit)
  SGZ.vs.GCL.results <- topTable(fit, number=Inf)
  
# Pair-wise t-test
 # t.test(exprs(SGZ.samples), exprs(GCL.samples), paired=T)

sig.DEGS <-row.names(subset(SGZ.vs.GCL.results, adj.P.Val<=0.05))
paste("There are",length(sig.DEGS),"significant DEGs between Macaque SGZ and GCL.")

## [1] "There are 0 significant DEGs between Macaque SGZ and GCL."

# Top logFC genes
topTable(fit, number=10, sort.by = "logFC")

##                logFC   AveExpr          t   P.Value adj.P.Val         B
## PDE7B     -0.9180228  8.713520 -0.7460964 0.4587068 0.9997796 -4.597508
## SLFN13    -0.9097828  9.888308 -0.6544719 0.5154671 0.9997796 -4.598198
## LOC696983 -0.8983702  9.857653 -0.6475556 0.5198988 0.9997796 -4.598247
## SV2B      -0.8928369  8.605551 -0.7263575 0.4706225 0.9997796 -4.597664
## CD36      -0.8237889  6.117680 -0.9247264 0.3590467 0.9997796 -4.595916
## LOC712107 -0.7955258  9.041498 -0.6338976 0.5287094 0.9997796 -4.598341
## ACVR1C    -0.7874374  7.286313 -0.7568770 0.4522729 0.9997796 -4.597422
## C2orf55   -0.7801166 10.330626 -0.5429380 0.5893111 0.9997796 -4.598920
## ANO3      -0.7715562 10.594549 -0.5300162 0.5981805 0.9997796 -4.598995
## PCSK2     -0.7449458  8.575641 -0.6173636 0.5394787 0.9997796 -4.598453

Candidate Genes

library(dplyr); library(ggplot2)
candidate.genes <- ex_mac[row.names(exprs(ex_mac)) %in% c("SOX11","SOX4","DCX","MKI67")][,pData(ex_mac)$age=="48 mo"]

copy.mac <- data.frame(t(data.frame(exprs(candidate.genes))))
#copy.mac$Region <-sub(".*_ *(.*?) *_.*", "\\1", colnames(exprs(candidate.genes)))
copy.mac$structure_name <-pData(candidate.genes)$structure_name
copy.mac$structure_acronym <-pData(candidate.genes)$structure_acronym
candidate.data <-copy.mac %>% group_by(structure_name,structure_acronym) %>% summarise(SOX11=mean(SOX11), DCX=mean(DCX), MKI67=mean(MKI67)) %>% data.frame()



# SOX11
data <-candidate.data[order(-candidate.data$SOX11),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=SOX11, fill=SOX11),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))

# Top 10 expression
ggplot(rbind(subset(data,structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=SOX11, fill=SOX11),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))+ labs(title="Regions with the top 10 expression:\nSOX11")

# DCX
data <-candidate.data[order(-candidate.data$DCX),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=DCX, fill=DCX),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))

## Top 10 expression
ggplot(rbind(subset(data,structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=DCX, fill=DCX),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))+ labs(title="Regions with the top 10 expression:\nDCX")

# MKI67
data <-candidate.data[order(-candidate.data$MKI67),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=MKI67, fill=MKI67),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))

## Top 10 expression
ggplot(rbind(subset(data, structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=MKI67, fill=MKI67),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5)) + labs(title="Regions with the top 10 expression:\nMIK67")

[Approach 1: Unsupervised Clustering]

PCA: All Genes

first.PC <-1; last.PC <-4

# Run PCA
  # full_PCA <- prcomp(exprs(ex_mac), scale.=T, center=F)
  # save(full_PCA, file="all.genes_PCA.rda")
  load("all.genes_PCA.rda")
# Get proportion of variance
per_PC1 <- round(summary(full_PCA)$importance[2,1]*100,2)
per_PC2 <- round(summary(full_PCA)$importance[2,2]*100,2)
# Create data.frame
full_PCA.data <- data.frame(full_PCA$rotation[,first.PC:last.PC])
full_PCA.data$Region <- pData(ex_mac)$structure_acronym
full_PCA.data$Age <- factor(pData(ex_mac)$age, c("E40","E50","E70","E80","E90","E120","0 mo","3 mo","12 mo","48 mo"),ordered=T)


# plot PCA
dat <-full_PCA.data
DGsg <-subset(dat,Region=="DGsg")
# By Region
### Plot
library(ggplot2)
ggplot(data=dat) + geom_point(aes(x=PC1, y=PC2, fill=Region, color=Region), size=2) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2, colour=Region), size=4, shape=15) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2), colour="black", size=4, shape=0) + labs(title="PCA: Colored by Region \n[SGZ samples outlined]", x=paste("PC1 (",per_PC1,"%)"), y=paste("PC2 (",per_PC2,"%)")) + theme(plot.title = element_text(hjust=0.5), legend.position="None")

## By Age
ggplot(data=dat) + geom_point(aes(x=PC1, y=PC2, fill=Age, color=Age), size=2) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2,colour=Age), size=4, shape=15) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2),colour="black", size=4, shape=0) + labs(title="PCA: Colored by Age \n[SGZ samples outlined]", x=paste("PC1 (",per_PC1,"%)"), y=paste("PC2 (",per_PC2,"%)")) + theme(plot.title = element_text(hjust=0.5))

# Get top PC1 loadings
PC1.load <-full_PCA$x[order(-abs(full_PCA$x[,1])),][,"PC1"]
write.csv(PC1.load[1:100],"PC1.allgenes.csv")
pander(PC1.load[1:10] , style='simple',justify='left')

Table continues below
LOC701831	RPLP0	LOC711964	RPS16	ACTG1	LOC708858	LOC710110
-79.14	-78.52	-78.51	-78.31	-78.24	-77.7	-77.66

RPS11	LOC697734	EEF1A1
-77.64	-77.61	-77.59

Unsupervised Clustering

Interactive Heatmap Instructions:
Hover cursor over cells to see info.
Click and drag a box over the area you want to zoom in on.
Click a column or row label to highlight just that column/row.
Double click to return to original view.

library(cluster); library(factoextra); library(magrittr)


# [1: Distance Methods]
unsup.clust_distance <- function(data){
res.dist <- get_dist(data, stand=T, method="pearson")
  library(d3heatmap) # use interactive heatmap instead
d3heatmap(res.dist, scale = "column", k_row=num_clusts, k_col=num_clusts)
#fviz_dist(res.dist, gradient=list(low="#00AFBB", mid="white", high="#FC4E07"))
}

# [2: Partitioning Methods]
# [[K-means clustering]]
unsup.clust_kmeans <-function(data.){
  #library("NbClust")
  #nb <- NbClust(data., distance="euclidean", min.nc=2, max.nc=10, method="kmeans")
  library("factoextra")
  # Conduct kmean using that number
  set.seed(123)
  km.res <- kmeans(data., num_clusts, nstart = 25) # Automatically extract # of clusters
  ## Visualize
  fviz_cluster(km.res, data = data.,
             ellipse.type = "convex",
             palette = "jco",
             ggtheme = theme_minimal(), repel=F)
}
  
# [[Partitioning Around Medoids (PAM) clustering]]
unsup.clust_PAM <-function(data.){
  library("cluster")
  pam.res <- pam(data., num_clusts)
  ## Visualize
  fviz_cluster(pam.res, data = data.,
             ellipse.type = "convex",
             palette = "jco",
             ggtheme = theme_minimal(), repel=T)
}


# [Hierarchical Methods]
# [[Hierarchical Clustering]]
unsup.clust_hierarchical <- function(data){
  library("factoextra")
  res.hc <- data %>%
    scale() %>%                    # Scale the data
    dist(method = "euclidean") %>% # Compute dissimilarity matrix
    hclust(method = "ward.D2")     # Compute hierachical clustering
  # Visualize using factoextra
  # Cut in 4 groups and color by groups
  fviz_dend(res.hc, k = num_clusts, # Cut in (#) groups
            cex = 0.5, repel=F, # label size
            palette="aaas",
            color_labels_by_k =T, # color labels by groups
            rect=T, # Add rectangle around groups
            type="circular", #"rectangle", "circular", "phylogenic".
            phylo_layout="layout.auto",# layout.auto, layout_with_drl, layout_as_tree, layout.gem, layout.mds, layout_with_lgl
            horiz=T, main="Unsupervised Hierarchical Clustering"
            )
  assign("hierarchical.output",res.hc)
}


# [[Hierarchical Clustering on Principal Components (HCPC)]]
unsup.clust_hcpc <- function(facto_data){
  library(FactoMineR)
  # Principal Component Analysis:
  res.pca <-  PCA(facto_data, graph=F)
    plot(res.pca)
  # Clustering, auto nb of clusters:
  hc <- HCPC(res.pca, nb.clust= -1)
    hc$call$t$nb.clust # Number of suggest clusters
  # Construct a hierarchical tree from a partition (with 10 clusters)
    # (useful when the number of individuals is very important)
    # hc2 <- HCPC(iris[,1:4], kk=10, nb.clust=-1)
}

All Genes

# Setup data
    unsup.eset <-ex_mac[,pData(ex_mac)$age=="48 mo"]
    my_data <- t(exprs(unsup.eset))
    row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
    # my_data <-full_PCA.data[,first.PC:last.PC]
    # row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate  number of clusters
    # est.clusts <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
      # save(est.clusts, file="estimated.clusters_all.genes.rda")
      load("estimated.clusters_all.genes.rda")
      est.clusts

      num_clusts=5 # Select number of clusters based on results

# Conduct unsupervised clustering
     unsup.clust_distance(my_data) # [1]

    # unsup.clust_kmeans(my_data) # [2]
    # unsup.clust_PAM(my_data) #[3]
    # unsup.clust_hierarchical(my_data) #[4]
    # unsup.clust_hcpc(my_data) # [5]

Gene Subset: Mouse SGZ-enriched (Table S1)

# Setup data
unsup.eset <-ex_mac[row.names(exprs(ex_mac)) %in% s1.genes, pData(ex_mac)$age=="48 mo"] 
    my_data <- t(exprs(unsup.eset))
    row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
    # my_data <-full_PCA.data[,first.PC:last.PC]
    # row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate  number of clusters
    est.clusts_S1.genes <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
    est.clusts_S1.genes

      num_clusts=3 # Select number of clusters based on results

# Conduct unsupervised clustering
    unsup.clust_distance(my_data) # [1]

    # unsup.clust_kmeans(my_data) # [2]
    # unsup.clust_PAM(my_data) #[3]
    # unsup.clust_hierarchical(my_data) #[4]
    # unsup.clust_hcpc(my_data) # [5]

Gene Subset: ‘Macaque Tan Module’ genes only

# Setup data
unsup.eset <-ex_mac[row.names(exprs(ex_mac)) %in% Mac_mod.tan, pData(ex_mac)$age=="48 mo"]
    my_data <- t(exprs(unsup.eset))
    row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
    # my_data <-full_PCA.data[,first.PC:last.PC]
    # row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate  number of clusters
    est.clusts_S4.genes <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
    est.clusts_S4.genes

      num_clusts=5 # Select number of clusters based on results

# Conduct unsupervised clustering
    unsup.clust_distance(my_data) # [1]

    # unsup.clust_kmeans(my_data) # [2]
    # unsup.clust_PAM(my_data) #[3]
    # unsup.clust_hierarchical(my_data) #[4]
    # unsup.clust_hcpc(my_data) # [5]

[Approach 2: Compare Expression Profiles]

Differential Expression

All Genes

library(limma); library(statmod)
SGZ.samples <-exprs(ex_mac[,pData(ex_mac)$structure_acronym==c("DGsg")& pData(ex_mac)$age=="48 mo"])

region_list <- as.character(unique(pData(ex_mac)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
   region.samples <-exprs( ex_mac[,pData(ex_mac)$structure_name==region & pData(ex_mac)$age=="48 mo"] )
   limma.data <-cbind(SGZ.samples, region.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
   DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] / 
     dim(tab)[1] *100,3)
   DGE_summary <- rbind(DGE_summary, 
                        data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
                             Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(tab$logFC)),
                             Total_t.stat=sum(abs(tab$t))) 
                        )
}

# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")

## [1] "61 / 128 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."

# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)

	Test	Sig_DEGs	Total_genes	Percent_DEGs	Total_logFC	Total_t.stat
69	SGZ vs. subgranular zone of dentate gyrus (cortex)	0	17555	0	8.799e-12	2.409e-12
73	SGZ vs. granular layer of dentate gyrus (cortex)	0	17555	0	1579	433.1
70	SGZ vs. stratum pyramidale of CA3	0	17555	0	3443	941.2
80	SGZ vs. polyform layer of dentate gyrus (cortex)	0	17555	0	3464	946.3
60	SGZ vs. stratum pyramidale of CA1	0	17555	0	3500	957.1
58	SGZ vs. stratum pyramidale of CA2	0	17555	0	3594	983.3
101	SGZ vs. subiculum	0	17555	0	3746	1024
74	SGZ vs. lateral nucleus	0	17555	0	4555	1236
104	SGZ vs. rostral periamygdaloid cortex (rPAC)	0	17555	0	4602	1249
44	SGZ vs. paralaminar nucleus	0	17555	0	4610	1251
103	SGZ vs. amygdalopiriform transition area	0	17555	0	4747	1287
106	SGZ vs. amygdalohippocampal area	0	17555	0	4823	1310
43	SGZ vs. stratum radiatum of CA1	0	17555	0	4840	1326
46	SGZ vs. accessory basal nucleus (basomedial nucleus)	0	17555	0	4890	1328
65	SGZ vs. medial nucleus	0	17555	0	4994	1353
105	SGZ vs. anterior amygdaloid area	0	17555	0	5193	1410
47	SGZ vs. basal nucleus (basolateral nucleus)	0	17555	0	5385	1463
107	SGZ vs. central amygdaloid nucleus	0	17555	0	5398	1468
90	SGZ vs. layer II of V1	0	17555	0	5675	1584
89	SGZ vs. layer III of V1	0	17555	0	5745	1602
98	SGZ vs. layer IVA of V1	0	17555	0	5798	1618
110	SGZ vs. layer II of V2	0	17555	0	5895	1644
114	SGZ vs. layer III of V2	0	17555	0	5918	1650
92	SGZ vs. layer II of rostral cingulate cortex	0	17555	0	5942	1659
68	SGZ vs. layer V of V1	0	17555	0	5955	1663
66	SGZ vs. layer V of rostral cingulate cortex	0	17555	0	5969	1665
112	SGZ vs. layer V of V2	0	17555	0	6009	1676
122	SGZ vs. layer V of caudal orbitofrontal cortex	0	17555	0	6019	1680
113	SGZ vs. layer VI of V2	0	17555	0	6032	1683
124	SGZ vs. layer II of dorsolateral prefrontal cortex	0	17555	0	6035	1683
100	SGZ vs. olfactory tubercle	0	17555	0	6037	1689
95	SGZ vs. layer IVCa of V1	0	17555	0	6104	1705
120	SGZ vs. layer II of caudal orbitofrontal cortex	0	17555	0	6107	1705
121	SGZ vs. layer III of caudal orbitofrontal cortex	0	17555	0	6132	1713
51	SGZ vs. layer VI of rostral cingulate cortex	0	17555	0	6139	1710
127	SGZ vs. layer V of dorsolateral prefrontal cortex	0	17555	0	6144	1715
96	SGZ vs. layer IVB of V1	0	17555	0	6159	1719
111	SGZ vs. granular layer IV of V2	0	17555	0	6166	1720
125	SGZ vs. layer III of dorsolateral prefrontal cortex	0	17555	0	6168	1722
71	SGZ vs. layer VI of V1	0	17555	0	6185	1729
126	SGZ vs. granular layer IV of dorsolateral prefrontal cortex	0	17555	0	6189	1728
108	SGZ vs. layer I of V1	0	17555	0	6191	1727
117	SGZ vs. granular layer IV of medial orbitofrontal cortex	0	17555	0	6237	1743
9	SGZ vs. putamen	0	17555	0	6271	1754
93	SGZ vs. layer III of rostral cingulate cortex	0	17555	0	6303	1761
99	SGZ vs. islands of Calleja	0	17555	0	6309	1765
128	SGZ vs. layer VI of dorsolateral prefrontal cortex	0	17555	0	6310	1761
94	SGZ vs. layer IVCb of V1	0	17555	0	6320	1766
123	SGZ vs. layer VI of caudal orbitofrontal cortex	0	17555	0	6413	1793
116	SGZ vs. layer III of medial orbitofrontal cortex	0	17555	0	6421	1796
118	SGZ vs. layer V of medial orbitofrontal cortex	0	17555	0	6429	1800
119	SGZ vs. layer VI of medial orbitofrontal cortex	0	17555	0	6525	1824
115	SGZ vs. layer II of medial orbitofrontal cortex	0	17555	0	6592	1848
31	SGZ vs. external segment of globus pallidus	0	17555	0	7077	1982
30	SGZ vs. internal segment of globus pallidus	0	17555	0	7728	2168
33	SGZ vs. internal capsule	0	17555	0	7758	2176
109	SGZ vs. white matter of V1	0	17555	0	8284	2322
102	SGZ vs. CA4 region	0	17555	0	27339	6825
91	SGZ vs. stratum oriens of CA1	0	17555	0	27349	6833
10	SGZ vs. nucleus accumbens	0	17555	0	29612	7546
3	SGZ vs. caudate nucleus	0	17555	0	29669	7567
1	SGZ vs. ventricular zone of V1	17555	17555	100	136550	1320635
2	SGZ vs. caudal ganglionic eminence	17555	17555	100	136550	1320635
4	SGZ vs. globus pallidus	17555	17555	100	136550	1320635
5	SGZ vs. cortical hem	17555	17555	100	136550	1320635
6	SGZ vs. medial ganglionic eminence	17555	17555	100	136550	1320635
7	SGZ vs. lateral ganglionic eminence	17555	17555	100	136550	1320635
8	SGZ vs. hippocampal subventricular zone of CA1	17555	17555	100	136550	1320635
11	SGZ vs. inner ventricular zone of rostral cingulate cortex	17555	17555	100	136550	1320635
12	SGZ vs. outer ventricular zone of rostral cingulate cortex	17555	17555	100	136550	1320635
13	SGZ vs. subventricular zone of rostral cingulate cortex	17555	17555	100	136550	1320635
14	SGZ vs. marginal zone of rostral cingulate cortex	17555	17555	100	136550	1320635
15	SGZ vs. hippocampal ventricular zone of CA1	17555	17555	100	136550	1320635
16	SGZ vs. subventricular zone of V1	17555	17555	100	136550	1320635
17	SGZ vs. marginal zone of CA1	17555	17555	100	136550	1320635
18	SGZ vs. amygdaloid complex	17555	17555	100	136550	1320635
19	SGZ vs. inner ventricular zone of V1	17555	17555	100	136550	1320635
20	SGZ vs. outer ventricular zone of V1	17555	17555	100	136550	1320635
21	SGZ vs. inner cortical plate (infragranular layer) of V1	17555	17555	100	136550	1320635
22	SGZ vs. marginal zone of V1	17555	17555	100	136550	1320635
23	SGZ vs. intermediate zone of V1	17555	17555	100	136550	1320635
24	SGZ vs. subplate zone of V1	17555	17555	100	136550	1320635
25	SGZ vs. dorsal lateral geniculate nucleus	17555	17555	100	136550	1320635
26	SGZ vs. dentate migratory stream	17555	17555	100	136550	1320635
27	SGZ vs. hippocampal subplate of CA1	17555	17555	100	136550	1320635
28	SGZ vs. hippocampal plate of CA1	17555	17555	100	136550	1320635
29	SGZ vs. granular layer anlage of dentate gyrus (cortex)	17555	17555	100	136550	1320635
32	SGZ vs. lateral ganglionic eminence-cortex border	17555	17555	100	136550	1320635
34	SGZ vs. outer fiber (plexiform) zone of rostral cingulate cortex	17555	17555	100	136550	1320635
35	SGZ vs. inner ventricular zone of S1	17555	17555	100	136550	1320635
36	SGZ vs. outer ventricular zone of S1	17555	17555	100	136550	1320635
37	SGZ vs. subventricular zone of S1	17555	17555	100	136550	1320635
38	SGZ vs. subplate zone of S1	17555	17555	100	136550	1320635
39	SGZ vs. inner cortical plate (infragranular layer) of S1	17555	17555	100	136550	1320635
40	SGZ vs. intermediate zone of rostral cingulate cortex	17555	17555	100	136550	1320635
41	SGZ vs. subplate zone of rostral cingulate cortex	17555	17555	100	136550	1320635
42	SGZ vs. inner cortical plate (infragranular layer) of rostral cingulate cortex	17555	17555	100	136550	1320635
45	SGZ vs. medial division of central nucleus	17555	17555	100	136550	1320635
48	SGZ vs. outer subventricular zone of rostral cingulate cortex	17555	17555	100	136550	1320635
49	SGZ vs. inner fiber (plexiform) zone of rostral cingulate cortex	17555	17555	100	136550	1320635
50	SGZ vs. outer cortical plate of rostral cingulate cortex	17555	17555	100	136550	1320635
52	SGZ vs. outer fiber (plexiform) zone of V1	17555	17555	100	136550	1320635
53	SGZ vs. inner subventricular zone of V1	17555	17555	100	136550	1320635
54	SGZ vs. outer subventricular zone of V1	17555	17555	100	136550	1320635
55	SGZ vs. intermediate cell dense zone of V1	17555	17555	100	136550	1320635
56	SGZ vs. pyramidal layer of subiculum	17555	17555	100	136550	1320635
57	SGZ vs. hippocampal intermediate zone of CA1	17555	17555	100	136550	1320635
59	SGZ vs. stratum lacunosum-moleculare of CA1	17555	17555	100	136550	1320635
61	SGZ vs. inner subventricular zone of S1	17555	17555	100	136550	1320635
62	SGZ vs. outer subventricular zone of S1	17555	17555	100	136550	1320635
63	SGZ vs. layer VI of S1	17555	17555	100	136550	1320635
64	SGZ vs. amygdaloid intramedullary gray	17555	17555	100	136550	1320635
67	SGZ vs. outer cortical plate of V1	17555	17555	100	136550	1320635
72	SGZ vs. lateral division of central nucleus	17555	17555	100	136550	1320635
75	SGZ vs. transitory migratory zone of V1	17555	17555	100	136550	1320635
76	SGZ vs. inner subventricular zone of rostral cingulate cortex	17555	17555	100	136550	1320635
77	SGZ vs. cortical plate of V1	17555	17555	100	136550	1320635
78	SGZ vs. ventricular zone of S1	17555	17555	100	136550	1320635
79	SGZ vs. dorsal lateral geniculate nucleus, parvocellular layers	17555	17555	100	136550	1320635
81	SGZ vs. inner fiber (plexiform) zone of V1	17555	17555	100	136550	1320635
82	SGZ vs. supragranular layer of V1	17555	17555	100	136550	1320635
83	SGZ vs. granular layer IV of V1	17555	17555	100	136550	1320635
84	SGZ vs. periamygdaloid cortex (cortical amygdaloid nucleus)	17555	17555	100	136550	1320635
85	SGZ vs. ventricular zone of rostral cingulate cortex	17555	17555	100	136550	1320635
86	SGZ vs. molecular layer of dentate gyrus (cortex)	17555	17555	100	136550	1320635
87	SGZ vs. dorsal lateral geniculate nucleus, magnocellular layers	17555	17555	100	136550	1320635
88	SGZ vs. outer cortical plate of S1	17555	17555	100	136550	1320635
97	SGZ vs. dorsal lateral geniculate nucleus, koniocellular layers	17555	17555	100	136550	1320635

Mouse SGZ-enriched genes (Table S1)

#Subset just the candidate genes and re-run t-tests
  ## Dividing.Cell genes (Miller et al. 2013, Table S1)
  gene.sub <- ex_mac[row.names(exprs(ex_mac)) %in% s1.genes, pData(ex_mac)$age=="48 mo"]
  
SGZ.samples <-exprs(gene.sub[,pData(gene.sub)$structure_acronym==c("DGsg")])

region_list <- as.character(unique(pData(gene.sub)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
   region.samples <-exprs( gene.sub[,pData(gene.sub)$structure_name==region] )
   limma.data <-cbind(SGZ.samples, region.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
   DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] / 
     dim(tab)[1] *100,3)
   DGE_summary <- rbind(DGE_summary, 
                        data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
                             Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(tab$logFC)),
                             Total_t.stat=sum(abs(tab$t))) 
                        )
}

# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")

## [1] "61 / 61 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."

# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)

	Test	Total_genes	Total_logFC	Total_t.stat
49	SGZ vs. subgranular zone of dentate gyrus (cortex)	250	1.327e-13	3.338e-14
44	SGZ vs. granular layer of dentate gyrus (cortex)	250	59.48	15.26
50	SGZ vs. stratum pyramidale of CA3	250	75.55	18.69
46	SGZ vs. stratum pyramidale of CA1	250	75.68	18.83
42	SGZ vs. stratum pyramidale of CA2	250	77.22	19.13
3	SGZ vs. layer V of rostral cingulate cortex	250	93.68	23.53
45	SGZ vs. subiculum	250	94.2	23.06
52	SGZ vs. paralaminar nucleus	250	97.19	23.74
16	SGZ vs. layer V of caudal orbitofrontal cortex	250	97.84	24.51
8	SGZ vs. layer V of dorsolateral prefrontal cortex	250	98.73	24.8
17	SGZ vs. layer VI of caudal orbitofrontal cortex	250	99.15	24.84
18	SGZ vs. layer VI of dorsolateral prefrontal cortex	250	100.5	25.05
1	SGZ vs. layer II of rostral cingulate cortex	250	100.5	25.22
2	SGZ vs. layer III of rostral cingulate cortex	250	100.7	25.33
58	SGZ vs. amygdalohippocampal area	250	100.9	24.53
12	SGZ vs. layer V of medial orbitofrontal cortex	250	101.4	25.49
23	SGZ vs. olfactory tubercle	250	101.6	25.5
4	SGZ vs. layer VI of rostral cingulate cortex	250	102	25.34
11	SGZ vs. granular layer IV of medial orbitofrontal cortex	250	102.3	25.73
13	SGZ vs. layer VI of medial orbitofrontal cortex	250	102.3	25.48
15	SGZ vs. layer III of caudal orbitofrontal cortex	250	103	25.96
14	SGZ vs. layer II of caudal orbitofrontal cortex	250	103.7	25.97
54	SGZ vs. lateral nucleus	250	104.1	25.34
7	SGZ vs. granular layer IV of dorsolateral prefrontal cortex	250	104.6	26.27
55	SGZ vs. amygdalopiriform transition area	250	105.8	25.64
6	SGZ vs. layer III of dorsolateral prefrontal cortex	250	106.1	26.66
41	SGZ vs. layer VI of V2	250	106.2	26.48
56	SGZ vs. rostral periamygdaloid cortex (rPAC)	250	106.4	25.76
10	SGZ vs. layer III of medial orbitofrontal cortex	250	106.4	26.76
9	SGZ vs. layer II of medial orbitofrontal cortex	250	106.7	26.84
57	SGZ vs. accessory basal nucleus (basomedial nucleus)	250	107.2	26.02
5	SGZ vs. layer II of dorsolateral prefrontal cortex	250	107.4	26.83
33	SGZ vs. layer V of V1	250	107.7	26.9
39	SGZ vs. layer V of V2	250	107.9	26.91
22	SGZ vs. putamen	250	108.2	27.21
47	SGZ vs. stratum radiatum of CA1	250	109.4	26.62
34	SGZ vs. layer VI of V1	250	109.5	27.42
43	SGZ vs. polyform layer of dentate gyrus (cortex)	250	109.7	26.49
27	SGZ vs. layer II of V1	250	110.4	27.65
28	SGZ vs. layer III of V1	250	110.6	27.7
36	SGZ vs. layer II of V2	250	111.1	27.74
59	SGZ vs. basal nucleus (basolateral nucleus)	250	112.2	27.25
38	SGZ vs. granular layer IV of V2	250	112.9	28.21
29	SGZ vs. layer IVA of V1	250	113.6	28.46
37	SGZ vs. layer III of V2	250	114	28.45
21	SGZ vs. islands of Calleja	250	114	28.49
60	SGZ vs. central amygdaloid nucleus	250	119.4	28.92
30	SGZ vs. layer IVB of V1	250	119.8	29.9
32	SGZ vs. layer IVCb of V1	250	120.5	30.24
53	SGZ vs. medial nucleus	250	122	29.5
31	SGZ vs. layer IVCa of V1	250	122.7	30.83
26	SGZ vs. layer I of V1	250	123.6	30.36
51	SGZ vs. anterior amygdaloid area	250	127.9	30.84
24	SGZ vs. external segment of globus pallidus	250	131.6	32.63
20	SGZ vs. internal capsule	250	135.9	34.08
25	SGZ vs. internal segment of globus pallidus	250	143.2	35.73
35	SGZ vs. white matter of V1	250	153.4	38.4
48	SGZ vs. stratum oriens of CA1	250	353.2	77.83
61	SGZ vs. CA4 region	250	396.4	89.38
40	SGZ vs. nucleus accumbens	250	417.1	95.1
19	SGZ vs. caudate nucleus	250	429.5	98.66

Macaque Tan module genes (Table S4)

#Subset just the candidate genes and re-run t-tests
  ## Dividing.Cell genes (Miller et al. 2013, Table S4)
  gene.sub <- ex_mac[row.names(exprs(ex_mac)) %in% Mac_mod.tan, pData(ex_mac)$age=="48 mo"]
  
SGZ.samples <-exprs(gene.sub[,pData(gene.sub)$structure_acronym==c("DGsg")])

region_list <- as.character(unique(pData(gene.sub)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
   region.samples <-exprs( gene.sub[,pData(gene.sub)$structure_name==region] )
   limma.data <-cbind(SGZ.samples, region.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
   DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] / 
     dim(tab)[1] *100,3)
   DGE_summary <- rbind(DGE_summary, 
                        data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
                             Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(tab$logFC)),
                             Total_t.stat=sum(abs(tab$t))) 
                        )
}

# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")

## [1] "61 / 61 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."

# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)

	Test	Total_genes	Total_logFC	Total_t.stat
49	SGZ vs. subgranular zone of dentate gyrus (cortex)	159	8.449e-14	2.223e-14
44	SGZ vs. granular layer of dentate gyrus (cortex)	159	24.2	6.45
50	SGZ vs. stratum pyramidale of CA3	159	44.14	11.54
58	SGZ vs. amygdalohippocampal area	159	45.72	11.87
56	SGZ vs. rostral periamygdaloid cortex (rPAC)	159	46.88	12.15
42	SGZ vs. stratum pyramidale of CA2	159	47.34	12.39
55	SGZ vs. amygdalopiriform transition area	159	47.79	12.37
46	SGZ vs. stratum pyramidale of CA1	159	48.55	12.72
54	SGZ vs. lateral nucleus	159	49.34	12.8
57	SGZ vs. accessory basal nucleus (basomedial nucleus)	159	49.57	12.87
53	SGZ vs. medial nucleus	159	52.54	13.55
45	SGZ vs. subiculum	159	52.66	13.68
26	SGZ vs. layer I of V1	159	53.09	13.92
23	SGZ vs. olfactory tubercle	159	53.83	14.3
22	SGZ vs. putamen	159	54.07	14.38
43	SGZ vs. polyform layer of dentate gyrus (cortex)	159	55	14.06
21	SGZ vs. islands of Calleja	159	55.16	14.58
14	SGZ vs. layer II of caudal orbitofrontal cortex	159	55.4	14.79
52	SGZ vs. paralaminar nucleus	159	55.69	14.34
5	SGZ vs. layer II of dorsolateral prefrontal cortex	159	56.15	14.94
1	SGZ vs. layer II of rostral cingulate cortex	159	56.49	15.11
51	SGZ vs. anterior amygdaloid area	159	57.02	14.68
59	SGZ vs. basal nucleus (basolateral nucleus)	159	57.44	14.89
47	SGZ vs. stratum radiatum of CA1	159	58.26	14.99
60	SGZ vs. central amygdaloid nucleus	159	58.29	15.02
36	SGZ vs. layer II of V2	159	58.35	15.56
9	SGZ vs. layer II of medial orbitofrontal cortex	159	58.61	15.7
27	SGZ vs. layer II of V1	159	58.88	15.73
4	SGZ vs. layer VI of rostral cingulate cortex	159	59.43	15.61
3	SGZ vs. layer V of rostral cingulate cortex	159	60.32	16.12
13	SGZ vs. layer VI of medial orbitofrontal cortex	159	60.78	16.03
10	SGZ vs. layer III of medial orbitofrontal cortex	159	61.23	16.41
2	SGZ vs. layer III of rostral cingulate cortex	159	61.98	16.63
12	SGZ vs. layer V of medial orbitofrontal cortex	159	62.69	16.76
28	SGZ vs. layer III of V1	159	62.84	16.78
11	SGZ vs. granular layer IV of medial orbitofrontal cortex	159	63.19	16.9
18	SGZ vs. layer VI of dorsolateral prefrontal cortex	159	63.24	16.66
17	SGZ vs. layer VI of caudal orbitofrontal cortex	159	63.36	16.83
33	SGZ vs. layer V of V1	159	63.39	16.84
39	SGZ vs. layer V of V2	159	63.48	16.84
32	SGZ vs. layer IVCb of V1	159	63.72	16.92
37	SGZ vs. layer III of V2	159	64.09	17.09
41	SGZ vs. layer VI of V2	159	64.13	16.92
16	SGZ vs. layer V of caudal orbitofrontal cortex	159	64.13	17.08
15	SGZ vs. layer III of caudal orbitofrontal cortex	159	64.33	17.31
29	SGZ vs. layer IVA of V1	159	64.35	17.15
8	SGZ vs. layer V of dorsolateral prefrontal cortex	159	65.17	17.39
6	SGZ vs. layer III of dorsolateral prefrontal cortex	159	65.35	17.52
7	SGZ vs. granular layer IV of dorsolateral prefrontal cortex	159	65.45	17.48
31	SGZ vs. layer IVCa of V1	159	66	17.55
30	SGZ vs. layer IVB of V1	159	66.17	17.57
38	SGZ vs. granular layer IV of V2	159	66.18	17.58
34	SGZ vs. layer VI of V1	159	66.39	17.62
24	SGZ vs. external segment of globus pallidus	159	67.85	17.82
25	SGZ vs. internal segment of globus pallidus	159	75.87	19.96
20	SGZ vs. internal capsule	159	76.19	19.98
35	SGZ vs. white matter of V1	159	81.26	21.1
48	SGZ vs. stratum oriens of CA1	159	231.5	54.13
61	SGZ vs. CA4 region	159	253	60.37
40	SGZ vs. nucleus accumbens	159	264.1	63.77
19	SGZ vs. caudate nucleus	159	269.1	65.27

[Approach 3: Compare Network Modules]

WGCNA: Macaque Microarray (SGZ/GCL samples only, all ages)

Attempt to recreate networks from Miller et al. 2013

  library(WGCNA)

## ==========================================================================
## *
## *  Package WGCNA 1.61 loaded.
## *
## *    Important note: It appears that your system supports multi-threading,
## *    but it is not enabled within WGCNA in R. 
## *    To allow multi-threading within WGCNA with all available cores, use 
## *
## *          allowWGCNAThreads()
## *
## *    within R. Use disableWGCNAThreads() to disable threading if necessary.
## *    Alternatively, set the following environment variable on your system:
## *
## *          ALLOW_WGCNA_THREADS=<number_of_processors>
## *
## *    for example 
## *
## *          ALLOW_WGCNA_THREADS=8
## *
## *    To set the environment variable in linux bash shell, type 
## *
## *           export ALLOW_WGCNA_THREADS=8
## *
## *     before running R. Other operating systems or shells will
## *     have a similar command to achieve the same aim.
## *
## ==========================================================================

  library(flashClust)
allowWGCNAThreads() # Enable multi-threading

## Allowing multi-threading with up to 8 threads.

all.SGZ.GCL <- ex_mac[,pData(ex_mac)$structure_acronym %in% c("DGsg","DGgr")]
datExpr <- exprs(all.SGZ.GCL)
#net_corrs <- function(eset1, eset2){


## Soft threshold
powers = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) 
sft = pickSoftThreshold(datExpr, networkType="signed", powerVector=powers) # t(net$MEs)

##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1    0.205 58.80        0.30000    48.4      48.4   48.8
## 2      2    0.215 30.10        0.32600    46.9      46.9   47.7
## 3      3    0.232 21.10        0.34000    45.4      45.5   46.6
## 4      4    0.258 17.30        0.36700    44.0      44.1   45.5
## 5      5    0.422 15.00        0.66000    42.7      42.8   44.5
## 6      6    0.427 12.70        0.65900    41.4      41.5   43.5
## 7      7    0.433 11.00        0.65000    40.2      40.3   42.5
## 8      8    0.438  9.74        0.64800    39.0      39.2   41.5
## 9      9    0.455  8.90        0.64900    37.8      38.1   40.6
## 10    10    0.462  8.18        0.63200    36.7      37.0   39.7
## 11    11    0.466  7.51        0.62900    35.7      36.0   38.8
## 12    12    0.477  7.12        0.60100    34.7      35.0   37.9
## 13    13    0.607  7.99        0.65800    33.7      34.0   37.1
## 14    14    0.219 31.60        0.00827    32.8      33.1   36.2

### Plot powers
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=0.9,col="red");

# this line corresponds to using an R^2 cut-off of h
  #abline(h=0.9,col="red")

## Run
POWER=14 # Same as Miller et al. 2013
# Assume defaults for everything not mentioned in Miller et al. 2013
   #net.mac.SGZ_GCL = blockwiseModules(t(datExpr), power=POWER, networkType="signed")
 #save(net.mac.SGZ_GCL, file="WGCNA_ABA.macaque_SGZ.GCL.rda")

#___________________________________________________________________________________#
# Plot WGCNA results
load("~/Desktop/Research/Signatures_of_Neurogensis/WGCNA_ABA.macaque_SGZ.GCL.rda")
# Plot
num_mods <- length(colnames(net.mac.SGZ_GCL$MEs))
moduleColorsAutomatic = WGCNA::labels2colors(net.mac.SGZ_GCL$colors)
mColors = moduleColorsAutomatic[net.mac.SGZ_GCL$blockGenes[[1]]]
plotDendroAndColors(net.mac.SGZ_GCL$dendrograms[[1]], colors=mColors, dendroLabels=F, 
                    groupLabels=c(paste("deepSplit = 2\n# modules =",num_mods)), addGuide=T,main="WGCNA Modules:\n Macaque SGZ & GCL samples")

# Get mutually exclusive modules assignments for each gene
mod_assignments <-data.frame(ModuleColors=net.mac.SGZ_GCL$colors, genes=row.names(datExpr))
mod_assignments <- mod_assignments[order(mod_assignments$ModuleColors),]
write.csv(mod_assignments, "module.gene.assignments_SGZ.GCL.csv")

Rough ‘% overlap with genes in Tan module’ metric

modules <-unique(mod_assignments$ModuleColors)
module.overlap_summary <-data.frame()

for(color in modules){
  one.module <-subset(mod_assignments,ModuleColors==color)
  percent_tan.overlap <-round(sum(Mac_mod.tan %in% as.character(one.module$genes)) / length(one.module$genes)*100,3)
  module.overlap_summary <- rbind(module.overlap_summary,data.frame(New_Module=color, New_Module_Size=length(one.module$genes), Percent_chance.overlap=length(one.module$genes)/dim(exprs(ex_mac))[1]*100, Percent_Tan.overlap=percent_tan.overlap))
}

pander::pander(module.overlap_summary[order(-module.overlap_summary$Percent_Tan.overlap),],style='simple',justify='left', split.table=Inf)

	New_Module	New_Module_Size	Percent_chance.overlap	Percent_Tan.overlap
19	magenta	476	2.711	13.87
32	sienna3	38	0.2165	7.895
7	darkmagenta	51	0.2905	7.843
15	grey60	146	0.8317	2.74
4	cyan	243	1.384	2.469
12	green	1021	5.816	2.449
22	orange	87	0.4956	2.299
17	lightgreen	142	0.8089	2.113
31	salmon	301	1.715	1.993
30	saddlebrown	67	0.3817	1.493
33	skyblue	67	0.3817	1.493
5	darkgreen	97	0.5525	1.031
13	greenyellow	389	2.216	0.771
14	grey	285	1.623	0.702
36	tan	340	1.937	0.588
27	purple	406	2.313	0.493
3	brown	1110	6.323	0.45
1	black	742	4.227	0.404
2	blue	1237	7.046	0.404
28	red	1014	5.776	0.296
40	yellow	1058	6.027	0.189
37	turquoise	6166	35.12	0.162
6	darkgrey	87	0.4956	0
8	darkolivegreen	51	0.2905	0
9	darkorange	81	0.4614	0
10	darkred	115	0.6551	0
11	darkturquoise	94	0.5355	0
16	lightcyan	157	0.8943	0
18	lightyellow	134	0.7633	0
20	mediumpurple3	23	0.131	0
21	midnightblue	231	1.316	0
23	orangered4	30	0.1709	0
24	paleturquoise	65	0.3703	0
25	pink	575	3.275	0
26	plum1	31	0.1766	0
29	royalblue	128	0.7291	0
34	skyblue3	34	0.1937	0
35	steelblue	66	0.376	0
38	violet	57	0.3247	0
39	white	77	0.4386	0
41	yellowgreen	36	0.2051	0

WGCNA: Macaque Microarray (all regions, all genes, all ages)

Create new networks using all samples

 datExpr <- exprs(ex_mac)
#net_corrs <- function(eset1, eset2){
  library(WGCNA)
  library(flashClust)

## Soft threshold
powers = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) 
sft = pickSoftThreshold(datExpr, networkType="signed", powerVector=powers) # t(net$MEs)

##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1    0.819 99.40          0.922    1770      1770   1790
## 2      2    0.820 50.20          0.939    1690      1690   1740
## 3      3    0.816 33.40          0.936    1610      1620   1680
## 4      4    0.811 25.00          0.934    1540      1550   1630
## 5      5    0.810 19.70          0.939    1470      1480   1570
## 6      6    0.801 16.80          0.945    1400      1420   1520
## 7      7    0.796 14.40          0.944    1340      1360   1470
## 8      8    0.793 12.60          0.944    1280      1300   1430
## 9      9    0.783 11.10          0.937    1230      1250   1380
## 10    10    0.786 10.50          0.945    1180      1200   1340
## 11    11    0.783  9.49          0.946    1130      1150   1300
## 12    12    0.776  8.62          0.944    1080      1100   1260
## 13    13    0.773  7.92          0.942    1030      1060   1220
## 14    14    0.769  7.33          0.941     991      1020   1180

### Plot powers
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=0.9,col="red");

# this line corresponds to using an R^2 cut-off of h
  #abline(h=0.9,col="red")

## Run
POWER=14 # Same as Miller et al. 2013
# Assume defaults for everything not mentioned in Miller et al. 2013
  #net.mac = blockwiseModules(t(datExpr), power=POWER, networkType="signed", deepSplit=2)
# save(net.mac, file="WGCNA_ABA.macaque_all.samples.rda")

#___________________________________________________________________________________#
# Plot WGCNA results
load("~/Desktop/Research/Signatures_of_Neurogensis/WGCNA_ABA.macaque_all.samples.rda")
# Plot
num_mods <- length(colnames(net.mac$MEs))
moduleColorsAutomatic = WGCNA::labels2colors(net.mac$colors)
mColors = moduleColorsAutomatic[net.mac$blockGenes[[1]]]
plotDendroAndColors(net.mac$dendrograms[[1]], colors=mColors, dendroLabels=F, 
                    groupLabels=c(paste("deepSplit = 2\n# modules =",num_mods)), addGuide=T,main="Cluster Dendrogram")

# Get mutually exclusive modules assignments for each gene
mod_assignments <-data.frame(ModuleColors=net.mac$colors, genes=row.names(datExpr))
mod_assignments <-mod_assignments[order(mod_assignments$ModuleColors),]
write.csv(mod_assignments, "module.gene.assignments_all.regions.csv")

Rough ‘% overlap with genes in Tan module’ metric

modules <-unique(mod_assignments$ModuleColors)
module.overlap_summary <-data.frame()

for(color in modules){
  one.module <-subset(mod_assignments,ModuleColors==color)
  percent_tan.overlap <-round(sum(Mac_mod.tan %in% as.character(one.module$genes)) / length(one.module$genes)*100,3)
  module.overlap_summary <- rbind(module.overlap_summary,data.frame(New_Module=color, New_Module_Size=length(one.module$genes), Percent_chance.overlap=length(one.module$genes)/dim(exprs(ex_mac))[1]*100, Percent_Tan.overlap=percent_tan.overlap))
}

pander::pander(module.overlap_summary[order(-module.overlap_summary$Percent_Tan.overlap),],style='simple',justify='left', split.table=Inf)

	New_Module	New_Module_Size	Percent_chance.overlap	Percent_Tan.overlap
12	green	997	5.679	4.714
27	red	656	3.737	4.116
22	orangered4	41	0.2336	2.439
18	lightyellow	232	1.322	2.155
10	darkred	196	1.116	2.041
14	grey	493	2.808	2.028
8	darkolivegreen	108	0.6152	1.852
13	greenyellow	378	2.153	1.852
21	orange	169	0.9627	1.775
23	paleturquoise	140	0.7975	1.429
34	steelblue	141	0.8032	1.418
25	plum1	75	0.4272	1.333
38	white	164	0.9342	1.22
16	lightcyan	252	1.435	1.19
39	yellow	1059	6.032	0.944
19	magenta	435	2.478	0.92
37	violet	136	0.7747	0.735
9	darkorange	168	0.957	0.595
6	darkgrey	174	0.9912	0.575
1	black	609	3.469	0.493
15	grey60	250	1.424	0.4
24	pink	519	2.956	0.385
20	midnightblue	272	1.549	0.368
4	cyan	289	1.646	0.346
36	turquoise	3413	19.44	0.293
3	brown	1450	8.26	0.276
2	blue	2188	12.46	0.183
5	darkgreen	192	1.094	0
7	darkmagenta	99	0.5639	0
11	darkturquoise	176	1.003	0
17	lightgreen	244	1.39	0
26	purple	409	2.33	0
28	royalblue	227	1.293	0
29	saddlebrown	159	0.9057	0
30	salmon	293	1.669	0
31	sienna3	90	0.5127	0
32	skyblue	162	0.9228	0
33	skyblue3	82	0.4671	0
35	tan	329	1.874	0
40	yellowgreen	89	0.507	0

NOTES: There’s almost no overlap between the new modules (derived from all macaque samples) and the tan module (derived from only macaque SGZ/GCL samples).

Network Preservation Analysis

# Subset all samples from SGZ or GCL
all.SGZ.GCL <- ex_mac[,pData(ex_mac)$structure_acronym %in% c("DGsg","DGgr")]
# Setup up inputs
multiExpr  = list(A1=list(data=t(exprs(ex_mac))), A2=list(data=t(exprs(all.SGZ.GCL))) )
multiColor = list(A1 = net.mac$colors)
# Run network preservation
mp <- modulePreservation(multiExpr, multiColor, referenceNetworks=1, verbose=3, networkType="signed", nPermutations=30, maxGoldModuleSize=100)
save(mp, file="network.preservation_all.samples.vs.GCL.SGZ.rda")

Plot network preservation scores

load("~/Desktop/Research/Signatures_of_Neurogensis/network.preservation_all.samples.vs.GCL.SGZ.rda")
# Summary stats
stats <- mp$preservation$Z$ref.A1$inColumnsAlsoPresentIn.A2 # All stats output
pander::pander(stats[order(-stats[,2]),c(1:2)][1:10,],style='simple',justify='left')

	moduleSize	Zsummary.pres
yellow	1000	40.12
turquoise	1000	29.84
brown	1000	26.32
magenta	326	25.12
green	770	22.88
red	435	21.55
lightcyan	179	19.75
lightyellow	162	18.31
grey60	173	16.48
lightgreen	163	15.11

 # Plot summary stats (Zsummary.pres)
  library(ggplot2)
ggplot(stats, aes(y=Zsummary.pres, x=factor(rownames(stats)),
                                 fill=factor(rownames(stats))) ) +
  geom_bar(stat="identity") + labs(title=paste("Network preservation scores"), y="Zsummary.pres", x="Network module") + theme(legend.position="none",axis.text.x = element_text(angle=45, hjust=1)) + scale_fill_manual(values=rownames(stats))

[Approach 4: Deconvolution]

Format scRNAseq reference database

Redo with a more relevant sc-RNA-seq reference database

# [Method 1]  
  gse2 <- getGEO("GSE71485", GSEMatrix=T) # imports 0 features...
  #show(gse2)

# [Method 2]
  library(GEOquery)
  filePaths <-getGEOSuppFiles("GSE71485")
  gse <-read.table(row.names(filePaths))
  
library(Biobase)
Biobase::exprs(gse2) <-data.frame(gse2)
gse.eset <-ExpressionSet(gse2, annotation ="hgu133plus2.db" )
library(EWCE)
  
Shin <- read_celltype_data("~/Desktop/Research/Signatures_of_Neurogensis/scRNAseq_datasets/GSE71485_Single_TPM.txt")



library(annotate)
# Create eset
ex_mac <- ExpressionSet(datExpr.mac)
# Create macaque phenoData:
sampleInfo.mac$unique_id <- factor(paste(sampleInfo.mac$donor_name, sampleInfo.mac$structure_acronym, sampleInfo.mac$well_id, sep="_"))

p <- data.frame(sampleInfo.mac)
rownames(p) <- sampleInfo.mac$unique_id
pdata <- AnnotatedDataFrame(p)
# Replace phenoData in mac ExpressionSet
phenoData(ex_mac) <- pdata
# Insert condition
ex_mac$treatment <- "Macaque"

ex_mac <-ex_mac[,colSums(is.na(exprs(ex_mac))) == 0] # Remove cols with NA

‘Mouse SGZ-enriched’ genes (Table S1)

Setup data

# Set up gene list
example_genelist <- S1$MouseGene

library("EWCE")
load(file="~/Desktop/Research/Deconvolution_of_Evolution/celltype_data.rda")
# Find mouse homologues of human genes
  data("mouse_to_human_homologs")
  m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
  mouse.hits = unique(m2h[m2h$MGI.symbol %in% example_genelist,"MGI.symbol"])
  mouse.bg  = unique(setdiff(m2h$MGI.symbol,mouse.hits))
  
paste(length(mouse.hits),"/",length(example_genelist),"candidate genes found in sc-RNA-seq reference database.")

## [1] "341 / 363 candidate genes found in sc-RNA-seq reference database."

Sub-cell type level

# Bootstrap significance testing:
## 1) Without controlling for transcript length and GC content
full_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=T)

## [1] "interneurons_Int10"
## [1] 0
## [1] "Fold enrichment: 2.19482408258267"
## [1] "Standard deviations from mean: 14.0130691674156"
## [1] ""
## [1] "interneurons_Int6"
## [1] 0
## [1] "Fold enrichment: 1.85257520115396"
## [1] "Standard deviations from mean: 11.2158270271469"
## [1] ""
## [1] "interneurons_Int9"
## [1] 0
## [1] "Fold enrichment: 1.95878786651468"
## [1] "Standard deviations from mean: 9.54077432172045"
## [1] ""
## [1] "interneurons_Int2"
## [1] 1e-04
## [1] "Fold enrichment: 1.5275445211573"
## [1] "Standard deviations from mean: 7.04677513191367"
## [1] ""
## [1] "interneurons_Int4"
## [1] 0.006
## [1] "Fold enrichment: 1.2205531572426"
## [1] "Standard deviations from mean: 2.85248747675033"
## [1] ""
## [1] "interneurons_Int1"
## [1] 0
## [1] "Fold enrichment: 1.81674537423394"
## [1] "Standard deviations from mean: 8.71008017983052"
## [1] ""
## [1] "interneurons_Int3"
## [1] 0
## [1] "Fold enrichment: 2.17136793226558"
## [1] "Standard deviations from mean: 12.5208453228451"
## [1] ""
## [1] "interneurons_Int13"
## [1] 0
## [1] "Fold enrichment: 1.8108479968661"
## [1] "Standard deviations from mean: 9.96500593937373"
## [1] ""
## [1] "interneurons_Int16"
## [1] 0
## [1] "Fold enrichment: 1.74998659424822"
## [1] "Standard deviations from mean: 11.0864251611685"
## [1] ""
## [1] "interneurons_Int14"
## [1] 0
## [1] "Fold enrichment: 1.53123958374113"
## [1] "Standard deviations from mean: 7.60370208974289"
## [1] ""
## [1] "interneurons_Int11"
## [1] 0
## [1] "Fold enrichment: 1.5821441869313"
## [1] "Standard deviations from mean: 6.45783327361339"
## [1] ""
## [1] "interneurons_Int5"
## [1] 0
## [1] "Fold enrichment: 2.21581828559141"
## [1] "Standard deviations from mean: 16.1995906005206"
## [1] ""
## [1] "interneurons_Int7"
## [1] 0
## [1] "Fold enrichment: 2.10807282777085"
## [1] "Standard deviations from mean: 15.2154122822341"
## [1] ""
## [1] "interneurons_Int8"
## [1] 0
## [1] "Fold enrichment: 1.62523845934985"
## [1] "Standard deviations from mean: 9.85586312608527"
## [1] ""
## [1] "interneurons_Int12"
## [1] 0
## [1] "Fold enrichment: 1.59812107721362"
## [1] "Standard deviations from mean: 8.34306235492575"
## [1] ""
## [1] "interneurons_Int15"
## [1] 0
## [1] "Fold enrichment: 1.53414431066053"
## [1] "Standard deviations from mean: 7.10081498697409"
## [1] ""
## [1] "pyramidal.SS_.none."
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL4"
## [1] 0.9792
## [1] ""
## [1] "pyramidal.SS_ClauPyr"
## [1] 0.9713
## [1] ""
## [1] "pyramidal.SS_S1PyrL5"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL23"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrDL"
## [1] 0.9896
## [1] ""
## [1] "pyramidal.SS_S1PyrL5a"
## [1] 0.9944
## [1] ""
## [1] "pyramidal.CA1_SubPyr"
## [1] 0.7803
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr1"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL6b"
## [1] 0.8845
## [1] ""
## [1] "pyramidal.SS_S1PyrL6"
## [1] 0.9976
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr2"
## [1] 1
## [1] ""
## [1] "pyramidal.CA1_CA1PyrInt"
## [1] 0.0057
## [1] "Fold enrichment: 1.17966982801049"
## [1] "Standard deviations from mean: 2.80456658085347"
## [1] ""
## [1] "pyramidal.CA1_CA2Pyr2"
## [1] 1
## [1] ""
## [1] "oligodendrocytes_Oligo1"
## [1] 0
## [1] "Fold enrichment: 1.78923077344274"
## [1] "Standard deviations from mean: 5.92568939282575"
## [1] ""
## [1] "oligodendrocytes_Oligo3"
## [1] 1e-04
## [1] "Fold enrichment: 1.5404012022018"
## [1] "Standard deviations from mean: 4.94273793567662"
## [1] ""
## [1] "oligodendrocytes_Oligo4"
## [1] 0
## [1] "Fold enrichment: 1.7024076579995"
## [1] "Standard deviations from mean: 6.88929124822889"
## [1] ""
## [1] "oligodendrocytes_Oligo2"
## [1] 0
## [1] "Fold enrichment: 1.5783799579134"
## [1] "Standard deviations from mean: 6.24861232193111"
## [1] ""
## [1] "oligodendrocytes_Oligo6"
## [1] 0
## [1] "Fold enrichment: 1.52821395738866"
## [1] "Standard deviations from mean: 6.44720172805403"
## [1] ""
## [1] "oligodendrocytes_Oligo5"
## [1] 0
## [1] "Fold enrichment: 1.49461265330592"
## [1] "Standard deviations from mean: 4.98914296686808"
## [1] ""
## [1] "microglia_Mgl1"
## [1] 0.0095
## [1] "Fold enrichment: 1.52911835533135"
## [1] "Standard deviations from mean: 2.63953062938961"
## [1] ""
## [1] "microglia_Mgl2"
## [1] 0.0405
## [1] "Fold enrichment: 1.38818640475282"
## [1] "Standard deviations from mean: 1.93286556306214"
## [1] ""
## [1] "microglia_Pvm1"
## [1] 0.4283
## [1] ""
## [1] "microglia_Pvm2"
## [1] 0.5602
## [1] ""
## [1] "endothelial.mural_Vsmc"
## [1] 0.4418
## [1] ""
## [1] "endothelial.mural_Vend2"
## [1] 0.471
## [1] ""
## [1] "endothelial.mural_Peric"
## [1] 0.3748
## [1] ""
## [1] "endothelial.mural_.none."
## [1] 0.5677
## [1] ""
## [1] "endothelial.mural_Vend1"
## [1] 0.4315
## [1] ""
## [1] "astrocytes.ependymal_Astro2"
## [1] 0
## [1] "Fold enrichment: 3.31168988565184"
## [1] "Standard deviations from mean: 18.1467356540887"
## [1] ""
## [1] "astrocytes.ependymal_Astro1"
## [1] 0
## [1] "Fold enrichment: 3.59260253988707"
## [1] "Standard deviations from mean: 16.5398102591399"
## [1] ""
## [1] "astrocytes.ependymal_.none."
## [1] 0
## [1] "Fold enrichment: 2.35255686707315"
## [1] "Standard deviations from mean: 11.9206737398829"
## [1] ""
## [1] "astrocytes.ependymal_Choroid"
## [1] 0.034
## [1] "Fold enrichment: 1.380293127569"
## [1] "Standard deviations from mean: 2.06452642472585"
## [1] ""
## [1] "astrocytes.ependymal_Epend"
## [1] 0.1421
## [1] ""

full_results.sub$results[order(full_results.sub$results$p),3:5][1:6,]

##                    p fold_change sd_from_mean
## interneurons_Int10 0    2.194824    14.013069
## interneurons_Int6  0    1.852575    11.215827
## interneurons_Int9  0    1.958788     9.540774
## interneurons_Int1  0    1.816745     8.710080
## interneurons_Int3  0    2.171368    12.520845
## interneurons_Int13 0    1.810848     9.965006

# Bootstrap plot
ewce.plot(full_results.sub$results,mtc_method="BH")

## 2) Controlling for transcript length and GC content (NOT WORKING)
### Need to convert gene list to human gene symbols
human.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"HGNC.symbol"])
human.bg = unique(setdiff(m2h$HGNC.symbol,human.hits))
# Bootstrap significance testing controlling for transcript length and GC content
#cont_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
 #                               human.hits=human.hits, human.bg=human.bg,
 #                                reps=10000, sub=T, geneSizeControl=T)
#ewce.plot(cont_results.sub$results,mtc_method="BH")

NOTES: EWCE deconvolution pretty nicely reconstructs cell types from ‘mouse SGZ-enriched’ genes.

Cell type level

# 1) Bootstrap significance testing without controlling for transcript length and GC content
full_results = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=F)

## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 2.09136588810587"
## [1] "Standard deviations from mean: 10.0845867070809"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.4713
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.78847125730897"
## [1] "Standard deviations from mean: 16.8515136328113"
## [1] ""
## [1] "microglia"
## [1] 0.0889
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.6166351855409"
## [1] "Standard deviations from mean: 7.25210931801897"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.9995
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""

ewce.plot(full_results$results ,mtc_method="BH")

# 1) Controlling for transcript length and GC content (NOT WORKING)
#cont_results = bootstrap.enrichment.test(sct_data=celltype_data,human.hits=human.hits,human.bg=human.bg,reps=reps,sub=F,geneSizeControl=T)
#ewce.plot(cont_results$results,mtc_method="BH")

‘Macaque tan module’ genes (Table S4)

Setup data

# Set up gene list
example_genelist <- Mac_mod.tan

library("EWCE")
load(file="~/Desktop/Research/Deconvolution_of_Evolution/celltype_data.rda")
# Find mouse homologues of human genes
  data("mouse_to_human_homologs")
  m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
  mouse.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"MGI.symbol"])
  mouse.bg  = unique(setdiff(m2h$MGI.symbol,mouse.hits))
  
paste(length(mouse.hits),"/",length(example_genelist),"candidate genes found in sc-RNA-seq reference database.")

## [1] "214 / 226 candidate genes found in sc-RNA-seq reference database."

Sub-cell type level

# Bootstrap significance testing:
## 1) Without controlling for transcript length and GC content
full_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=T)

## [1] "interneurons_Int10"
## [1] 0.0022
## [1] "Fold enrichment: 1.37542231348334"
## [1] "Standard deviations from mean: 3.52907489412147"
## [1] ""
## [1] "interneurons_Int6"
## [1] 0.0388
## [1] "Fold enrichment: 1.19093877316789"
## [1] "Standard deviations from mean: 1.95952748583597"
## [1] ""
## [1] "interneurons_Int9"
## [1] 0.0954
## [1] ""
## [1] "interneurons_Int2"
## [1] 0.9201
## [1] ""
## [1] "interneurons_Int4"
## [1] 0.0752
## [1] ""
## [1] "interneurons_Int1"
## [1] 0.456
## [1] ""
## [1] "interneurons_Int3"
## [1] 0.6313
## [1] ""
## [1] "interneurons_Int13"
## [1] 0.4101
## [1] ""
## [1] "interneurons_Int16"
## [1] 0.0361
## [1] "Fold enrichment: 1.15770545822542"
## [1] "Standard deviations from mean: 1.84888589055422"
## [1] ""
## [1] "interneurons_Int14"
## [1] 0.0371
## [1] "Fold enrichment: 1.1677106808156"
## [1] "Standard deviations from mean: 1.91397854372934"
## [1] ""
## [1] "interneurons_Int11"
## [1] 0.0149
## [1] "Fold enrichment: 1.29210555920499"
## [1] "Standard deviations from mean: 2.54605410331782"
## [1] ""
## [1] "interneurons_Int5"
## [1] 0.0955
## [1] ""
## [1] "interneurons_Int7"
## [1] 0
## [1] "Fold enrichment: 1.46479711630888"
## [1] "Standard deviations from mean: 5.05665145687398"
## [1] ""
## [1] "interneurons_Int8"
## [1] 0.0198
## [1] "Fold enrichment: 1.16908842841573"
## [1] "Standard deviations from mean: 2.13828346706708"
## [1] ""
## [1] "interneurons_Int12"
## [1] 0.0115
## [1] "Fold enrichment: 1.22101604795294"
## [1] "Standard deviations from mean: 2.48330083181076"
## [1] ""
## [1] "interneurons_Int15"
## [1] 0.0892
## [1] ""
## [1] "pyramidal.SS_.none."
## [1] 0.0447
## [1] "Fold enrichment: 1.11291761699782"
## [1] "Standard deviations from mean: 1.71485786508396"
## [1] ""
## [1] "pyramidal.SS_S1PyrL4"
## [1] 0.1121
## [1] ""
## [1] "pyramidal.SS_ClauPyr"
## [1] 0.0435
## [1] "Fold enrichment: 1.25962438397466"
## [1] "Standard deviations from mean: 1.84169436784738"
## [1] ""
## [1] "pyramidal.SS_S1PyrL5"
## [1] 0.0802
## [1] ""
## [1] "pyramidal.SS_S1PyrL23"
## [1] 0.3145
## [1] ""
## [1] "pyramidal.SS_S1PyrDL"
## [1] 0.0532
## [1] ""
## [1] "pyramidal.SS_S1PyrL5a"
## [1] 0.1013
## [1] ""
## [1] "pyramidal.CA1_SubPyr"
## [1] 0.0757
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr1"
## [1] 0.0205
## [1] "Fold enrichment: 1.16088323218451"
## [1] "Standard deviations from mean: 2.13445904485584"
## [1] ""
## [1] "pyramidal.SS_S1PyrL6b"
## [1] 0.2159
## [1] ""
## [1] "pyramidal.SS_S1PyrL6"
## [1] 0.0045
## [1] "Fold enrichment: 1.27575337447446"
## [1] "Standard deviations from mean: 3.26656900134757"
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr2"
## [1] 0.2676
## [1] ""
## [1] "pyramidal.CA1_CA1PyrInt"
## [1] 0.0022
## [1] "Fold enrichment: 1.26042242174832"
## [1] "Standard deviations from mean: 3.24988576786636"
## [1] ""
## [1] "pyramidal.CA1_CA2Pyr2"
## [1] 0.274
## [1] ""
## [1] "oligodendrocytes_Oligo1"
## [1] 1e-04
## [1] "Fold enrichment: 1.86496184531441"
## [1] "Standard deviations from mean: 5.25564195094678"
## [1] ""
## [1] "oligodendrocytes_Oligo3"
## [1] 0
## [1] "Fold enrichment: 1.87659487116985"
## [1] "Standard deviations from mean: 6.41764405238795"
## [1] ""
## [1] "oligodendrocytes_Oligo4"
## [1] 0
## [1] "Fold enrichment: 1.70396316071085"
## [1] "Standard deviations from mean: 5.42162517490978"
## [1] ""
## [1] "oligodendrocytes_Oligo2"
## [1] 0
## [1] "Fold enrichment: 1.85850423772716"
## [1] "Standard deviations from mean: 7.33243443335636"
## [1] ""
## [1] "oligodendrocytes_Oligo6"
## [1] 0
## [1] "Fold enrichment: 1.59232254861078"
## [1] "Standard deviations from mean: 5.71570894832624"
## [1] ""
## [1] "oligodendrocytes_Oligo5"
## [1] 5e-04
## [1] "Fold enrichment: 1.53884964555229"
## [1] "Standard deviations from mean: 4.22554544618381"
## [1] ""
## [1] "microglia_Mgl1"
## [1] 0.0138
## [1] "Fold enrichment: 1.67487138682249"
## [1] "Standard deviations from mean: 2.63425387999052"
## [1] ""
## [1] "microglia_Mgl2"
## [1] 0.08
## [1] ""
## [1] "microglia_Pvm1"
## [1] 0.4666
## [1] ""
## [1] "microglia_Pvm2"
## [1] 0.223
## [1] ""
## [1] "endothelial.mural_Vsmc"
## [1] 0.1736
## [1] ""
## [1] "endothelial.mural_Vend2"
## [1] 0.0228
## [1] "Fold enrichment: 1.40967116962043"
## [1] "Standard deviations from mean: 2.21956100232789"
## [1] ""
## [1] "endothelial.mural_Peric"
## [1] 0.0174
## [1] "Fold enrichment: 1.50660371475035"
## [1] "Standard deviations from mean: 2.46302995459228"
## [1] ""
## [1] "endothelial.mural_.none."
## [1] 0.0647
## [1] ""
## [1] "endothelial.mural_Vend1"
## [1] 0.0023
## [1] "Fold enrichment: 1.76123299275408"
## [1] "Standard deviations from mean: 3.54840899717605"
## [1] ""
## [1] "astrocytes.ependymal_Astro2"
## [1] 0
## [1] "Fold enrichment: 1.98488688740207"
## [1] "Standard deviations from mean: 6.21280188504832"
## [1] ""
## [1] "astrocytes.ependymal_Astro1"
## [1] 0.0016
## [1] "Fold enrichment: 1.79600659835762"
## [1] "Standard deviations from mean: 3.98774558875209"
## [1] ""
## [1] "astrocytes.ependymal_.none."
## [1] 0
## [1] "Fold enrichment: 1.92448169917057"
## [1] "Standard deviations from mean: 6.5041993182344"
## [1] ""
## [1] "astrocytes.ependymal_Choroid"
## [1] 0.0139
## [1] "Fold enrichment: 1.60908586487711"
## [1] "Standard deviations from mean: 2.63564990314784"
## [1] ""
## [1] "astrocytes.ependymal_Epend"
## [1] 0.106
## [1] ""

full_results.sub$results[order(full_results.sub$results$p),3:5][1:6,]

##                             p fold_change sd_from_mean
## interneurons_Int7           0    1.464797     5.056651
## oligodendrocytes_Oligo3     0    1.876595     6.417644
## oligodendrocytes_Oligo4     0    1.703963     5.421625
## oligodendrocytes_Oligo2     0    1.858504     7.332434
## oligodendrocytes_Oligo6     0    1.592323     5.715709
## astrocytes.ependymal_Astro2 0    1.984887     6.212802

# Bootstrap plot
ewce.plot(full_results.sub$results,mtc_method="BH")

## 2) Controlling for transcript length and GC content (NOT WORKING)
### Need to convert gene list to human gene symbols
human.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"HGNC.symbol"])
human.bg = unique(setdiff(m2h$HGNC.symbol,human.hits))
# Bootstrap significance testing controlling for transcript length and GC content
#cont_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
 #                               human.hits=human.hits, human.bg=human.bg,
 #                                reps=10000, sub=T, geneSizeControl=T)
#ewce.plot(cont_results.sub$results,mtc_method="BH")

NOTES: EWCE deconvolution pretty nicely reconstructs cell types from ‘mouse SGZ-enriched’ genes.

Cell type level

# 1) Bootstrap significance testing without controlling for transcript length and GC content
full_results = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=F)

## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.6344116649678"
## [1] "Standard deviations from mean: 4.60910793868818"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.0018
## [1] "Fold enrichment: 1.4747216946214"
## [1] "Standard deviations from mean: 3.24823103769571"
## [1] ""
## [1] "interneurons"
## [1] 0.0066
## [1] "Fold enrichment: 1.14732797782528"
## [1] "Standard deviations from mean: 2.48761425945757"
## [1] ""
## [1] "microglia"
## [1] 0.0737
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.75891797699607"
## [1] "Standard deviations from mean: 7.06881439891116"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.0374
## [1] "Fold enrichment: 1.1169673151793"
## [1] "Standard deviations from mean: 1.80018737393311"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.0142
## [1] "Fold enrichment: 1.1382324582905"
## [1] "Standard deviations from mean: 2.24844729720863"
## [1] ""

ewce.plot(full_results$results ,mtc_method="BH")

# 1) Controlling for transcript length and GC content (NOT WORKING)
#cont_results = bootstrap.enrichment.test(sct_data=celltype_data,human.hits=human.hits,human.bg=human.bg,reps=reps,sub=F,geneSizeControl=T)
#ewce.plot(cont_results$results,mtc_method="BH")

Iteratively run EWCE on each region’s transcriptome

Run DGE: Each region vs. all other regions

samples <-ex_mac[,pData(ex_mac)$age=="48 mo"]

region_list <- as.character(unique(pData(samples)$structure_acronym))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
    other.samples <-exprs( samples[,pData(samples)$structure_acronym!=region] )
   region.samples <-exprs( samples[,pData(samples)$structure_acronym==region] )
  # Run DGE
   limma.data <-cbind(region.samples, other.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(region.samples)), rep(-1,ncol(other.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
    tab <- tab[order(-abs(tab$logFC)),]
  
   DGE_results[[paste(region,"vs. all other regions")]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(sig / total *100,3)
   DGE_summary <- rbind(DGE_summary,data.frame(Test=paste(region,"vs. all other regions"),Sig_DEGs=sig, Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(subset(tab, adj.P.Val<=0.05)$logFC)) ) 
                        )
}

# Print written summary
per_DEG <-length(subset(DGE_summary, percent_DEGs==100)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(per_DEG,"/",total_regions,"adult brain regions have ALL genes differentially expressed.")

## [1] "61 / 61 adult brain regions have ALL genes differentially expressed."

# Results table
pander::pander(subset(DGE_summary[order(c(DGE_summary$Percent_DEGs, -DGE_summary$Total_logFC)),], Test!="<NA>"),style='simple', justify='left', split.table=Inf)

Test	Sig_DEGs	Total_genes	Percent_DEGs	Total_logFC
rCG2 vs. all other regions	17555	17555	100	128972
rCG3 vs. all other regions	17555	17555	100	128975
rCG5 vs. all other regions	17555	17555	100	128966
rCG6 vs. all other regions	17555	17555	100	128954
dlPF2 vs. all other regions	17555	17555	100	128969
dlPF3 vs. all other regions	17555	17555	100	128971
dlPF4 vs. all other regions	17555	17555	100	128970
dlPF5 vs. all other regions	17555	17555	100	128970
mOF2 vs. all other regions	17555	17555	100	129003
mOF3 vs. all other regions	17555	17555	100	128990
mOF4 vs. all other regions	17555	17555	100	128985
mOF5 vs. all other regions	17555	17555	100	128994
mOF6 vs. all other regions	17555	17555	100	128982
cOF2 vs. all other regions	17555	17555	100	128977
cOF3 vs. all other regions	17555	17555	100	128978
cOF5 vs. all other regions	17555	17555	100	128973
cOF6 vs. all other regions	17555	17555	100	128982
dlPF6 vs. all other regions	17555	17555	100	128971
Ca vs. all other regions	17555	17555	100	130460
ic vs. all other regions	17555	17555	100	129026
IsCj vs. all other regions	17555	17555	100	129014
Pu vs. all other regions	17555	17555	100	129009
Tu vs. all other regions	17555	17555	100	129009
GPe vs. all other regions	17555	17555	100	129012
GPi vs. all other regions	17555	17555	100	129024
V1-1 vs. all other regions	17555	17555	100	128987
V1-2 vs. all other regions	17555	17555	100	128998
V1-3 vs. all other regions	17555	17555	100	128993
V1-4A vs. all other regions	17555	17555	100	128994
V1-4B vs. all other regions	17555	17555	100	128996
V14Ca vs. all other regions	17555	17555	100	129001
V14Cb vs. all other regions	17555	17555	100	129005
V1-5 vs. all other regions	17555	17555	100	129001
V1-6 vs. all other regions	17555	17555	100	129010
V1wm vs. all other regions	17555	17555	100	129026
V2-2 vs. all other regions	17555	17555	100	128996
V2-3 vs. all other regions	17555	17555	100	128991
V2-4 vs. all other regions	17555	17555	100	128996
V2_L5 vs. all other regions	17555	17555	100	128994
NAC vs. all other regions	17555	17555	100	130458
V2-6 vs. all other regions	17555	17555	100	128996
CA2py vs. all other regions	17555	17555	100	128813
DGpf vs. all other regions	17555	17555	100	128794
DGgr vs. all other regions	17555	17555	100	128823
S vs. all other regions	17555	17555	100	128795
CA1py vs. all other regions	17555	17555	100	128807
CA1ra vs. all other regions	17555	17555	100	128809
CA1or vs. all other regions	17555	17555	100	130332
DGsg vs. all other regions	17555	17555	100	128807
CA3py vs. all other regions	17555	17555	100	128804
AA vs. all other regions	17555	17555	100	128696
PL vs. all other regions	17555	17555	100	128700
Me vs. all other regions	17555	17555	100	128681
L vs. all other regions	17555	17555	100	128699
APir vs. all other regions	17555	17555	100	128696
rPAC vs. all other regions	17555	17555	100	128696
AB vs. all other regions	17555	17555	100	128699
AHA vs. all other regions	17555	17555	100	128703
B vs. all other regions	17555	17555	100	128700
CE vs. all other regions	17555	17555	100	128703
CA4 vs. all other regions	17555	17555	100	130333

Run EWCE for each region

## [1] "astrocytes-ependymal"
## [1] 0.69
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.74
## [1] ""
## [1] "endothelial-mural"
## [1] 0.49
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.36149578773616"
## [1] "Standard deviations from mean: 7.73043290085362"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.48706613495683"
## [1] "Standard deviations from mean: 5.39412006371251"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.3006910218487"
## [1] "Standard deviations from mean: 5.41294367034982"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30306114510364"
## [1] "Standard deviations from mean: 5.52351082686908"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.73
## [1] ""
## [1] "endothelial-mural"
## [1] 0.35
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.3700680438909"
## [1] "Standard deviations from mean: 8.82309287938557"
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33279184192791"
## [1] "Standard deviations from mean: 3.41109767616896"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33815230296478"
## [1] "Standard deviations from mean: 5.98798414409446"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.27629280460224"
## [1] "Standard deviations from mean: 5.37088813639783"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.49
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.88
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.28
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.29227703613989"
## [1] "Standard deviations from mean: 6.19301474960127"
## [1] ""
## [1] "microglia"
## [1] 0.16
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.32885384541189"
## [1] "Standard deviations from mean: 3.80231261579436"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.29004148629611"
## [1] "Standard deviations from mean: 5.18060664161643"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24766903001379"
## [1] "Standard deviations from mean: 4.60126470944077"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.04
## [1] "Fold enrichment: 1.20971624101054"
## [1] "Standard deviations from mean: 1.81828033235132"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.16
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.27
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.78
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.96
## [1] ""
## [1] "endothelial-mural"
## [1] 0.86
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.50224653938415"
## [1] "Standard deviations from mean: 10.3053919303254"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.33
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.35948375100978"
## [1] "Standard deviations from mean: 6.40297990697599"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.4582057192322"
## [1] "Standard deviations from mean: 8.9655423773847"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.57
## [1] ""
## [1] "endothelial-mural"
## [1] 0.93
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.81
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31816169598577"
## [1] "Standard deviations from mean: 6.33899284234282"
## [1] ""
## [1] "microglia"
## [1] 0.23
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37552893584322"
## [1] "Standard deviations from mean: 4.52055576250819"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.18244959149805"
## [1] "Standard deviations from mean: 3.08959177183019"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.25913868902883"
## [1] "Standard deviations from mean: 5.01154847532714"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.87
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.64
## [1] ""
## [1] "endothelial-mural"
## [1] 0.23
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.36050804340364"
## [1] "Standard deviations from mean: 7.24795800993131"
## [1] ""
## [1] "microglia"
## [1] 0.18
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.35692691069638"
## [1] "Standard deviations from mean: 4.40146608043808"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28986462214001"
## [1] "Standard deviations from mean: 5.52065620741554"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24423875640361"
## [1] "Standard deviations from mean: 4.54606290633971"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.91
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.65
## [1] ""
## [1] "endothelial-mural"
## [1] 0.76
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31295017479235"
## [1] "Standard deviations from mean: 6.17871887421864"
## [1] ""
## [1] "microglia"
## [1] 0.62
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.32522978286158"
## [1] "Standard deviations from mean: 3.49998495408187"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33746135195364"
## [1] "Standard deviations from mean: 6.49826352264873"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.29597272196383"
## [1] "Standard deviations from mean: 6.08073710174888"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.67
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.89
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.14
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.22651466651052"
## [1] "Standard deviations from mean: 4.01167772102314"
## [1] ""
## [1] "microglia"
## [1] 0.26
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.43042105463237"
## [1] "Standard deviations from mean: 4.35821391837049"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.24786678263757"
## [1] "Standard deviations from mean: 4.37249239022044"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.23438255030681"
## [1] "Standard deviations from mean: 4.56287400363116"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.75
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.91
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.97
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43380662021178"
## [1] "Standard deviations from mean: 7.73867774850157"
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.3834688946679"
## [1] "Standard deviations from mean: 3.8713145386966"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.35458476242393"
## [1] "Standard deviations from mean: 6.50205763876491"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3680353776727"
## [1] "Standard deviations from mean: 6.14124278687038"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.9
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.89
## [1] ""
## [1] "endothelial-mural"
## [1] 0.73
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44843776981431"
## [1] "Standard deviations from mean: 8.96532085343343"
## [1] ""
## [1] "microglia"
## [1] 0.59
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37377953042244"
## [1] "Standard deviations from mean: 4.57305150798601"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.31812101054864"
## [1] "Standard deviations from mean: 5.22532442557582"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.33090714349838"
## [1] "Standard deviations from mean: 5.80603267628215"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.34759803172799"
## [1] "Standard deviations from mean: 6.79346066332768"
## [1] ""
## [1] "microglia"
## [1] 0.51
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.25766584745416"
## [1] "Standard deviations from mean: 2.56206833777982"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.36339185402269"
## [1] "Standard deviations from mean: 7.11731453956082"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3373779339942"
## [1] "Standard deviations from mean: 6.45608084307466"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 0.73
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38137625614076"
## [1] "Standard deviations from mean: 7.51625656165242"
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.03
## [1] "Fold enrichment: 1.25493492942026"
## [1] "Standard deviations from mean: 2.40834151792195"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.39930295518793"
## [1] "Standard deviations from mean: 6.81153007756045"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.39106929297621"
## [1] "Standard deviations from mean: 7.78880036008649"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.11
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.69
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.5621222004961"
## [1] "Standard deviations from mean: 12.5150093541627"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.43
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.40467918113106"
## [1] "Standard deviations from mean: 6.80783994245793"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.45913555622893"
## [1] "Standard deviations from mean: 8.29765048082985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.66
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.87
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.74
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44795242248311"
## [1] "Standard deviations from mean: 8.45665807903486"
## [1] ""
## [1] "microglia"
## [1] 0.71
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.51039283321374"
## [1] "Standard deviations from mean: 5.93001116533696"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32773122803151"
## [1] "Standard deviations from mean: 5.67429368859355"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.36909390523736"
## [1] "Standard deviations from mean: 5.91227369102989"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.78
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.61
## [1] ""
## [1] "endothelial-mural"
## [1] 0.36
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39486214796231"
## [1] "Standard deviations from mean: 7.37205193558631"
## [1] ""
## [1] "microglia"
## [1] 0.12
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.24482744229959"
## [1] "Standard deviations from mean: 2.53644643417514"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28943834448305"
## [1] "Standard deviations from mean: 6.23199336799741"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.21958585957237"
## [1] "Standard deviations from mean: 4.64186024386257"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.75
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.36
## [1] ""
## [1] "endothelial-mural"
## [1] 0.13
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30844483976371"
## [1] "Standard deviations from mean: 6.24877224873451"
## [1] ""
## [1] "microglia"
## [1] 0.57
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.34055306650585"
## [1] "Standard deviations from mean: 3.75054657923674"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33344011129399"
## [1] "Standard deviations from mean: 5.6710161103249"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.2992959543193"
## [1] "Standard deviations from mean: 5.77464691151524"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.31
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.85
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.59
## [1] ""
## [1] "endothelial-mural"
## [1] 0.59
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.45991365654631"
## [1] "Standard deviations from mean: 8.33501477494695"
## [1] ""
## [1] "microglia"
## [1] 0.76
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.25
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33185773870414"
## [1] "Standard deviations from mean: 5.27417113038539"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.35989210449217"
## [1] "Standard deviations from mean: 6.03018789238473"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.15
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.62
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.79
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.93
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.49085975083854"
## [1] "Standard deviations from mean: 10.2236170140348"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.57
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32092329330656"
## [1] "Standard deviations from mean: 5.88231463180095"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3579236911614"
## [1] "Standard deviations from mean: 6.5865384050543"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.13
## [1] ""
## [1] "endothelial-mural"
## [1] 0.15
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.47
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.98
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.95
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38997916859193"
## [1] "Standard deviations from mean: 8.11429841638261"
## [1] ""
## [1] "microglia"
## [1] 0.51
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.17332537916424"
## [1] "Standard deviations from mean: 1.90264302432939"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32356707887264"
## [1] "Standard deviations from mean: 6.09647703284844"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.31144411926353"
## [1] "Standard deviations from mean: 5.94006264918281"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.38551373175015"
## [1] "Standard deviations from mean: 2.92079510642893"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.01
## [1] "Fold enrichment: 1.46018622778369"
## [1] "Standard deviations from mean: 2.57631933103226"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 2.16112033661971"
## [1] "Standard deviations from mean: 13.3692834699465"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.57188130086226"
## [1] "Standard deviations from mean: 11.3201395537844"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.55138764316653"
## [1] "Standard deviations from mean: 10.1874641509226"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.67894614152506"
## [1] "Standard deviations from mean: 13.4887628005985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4031458891024"
## [1] "Standard deviations from mean: 3.13677762894169"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.13
## [1] ""
## [1] "interneurons"
## [1] 0.93
## [1] ""
## [1] "microglia"
## [1] 0.72
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.89
## [1] ""
## [1] "endothelial-mural"
## [1] 0.44
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.27628832509747"
## [1] "Standard deviations from mean: 5.2235438343547"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.29
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.38776064644479"
## [1] "Standard deviations from mean: 6.3662912677179"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.55067257381571"
## [1] "Standard deviations from mean: 9.51482252806637"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.09
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.47
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.12
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.76
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.37379638148696"
## [1] "Standard deviations from mean: 6.2052097514887"
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.28
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.23137724758419"
## [1] "Standard deviations from mean: 3.76432407243025"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.41765917241121"
## [1] "Standard deviations from mean: 7.30602505474287"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.12
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.32
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.94
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.85
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.33845119170987"
## [1] "Standard deviations from mean: 6.47556238057059"
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.08
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.30421348835203"
## [1] "Standard deviations from mean: 5.3959306894098"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43080122001797"
## [1] "Standard deviations from mean: 7.41109266741336"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.31319351819271"
## [1] "Standard deviations from mean: 2.48344244904012"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.38462282582869"
## [1] "Standard deviations from mean: 3.06937544847777"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.01
## [1] "Fold enrichment: 1.43441833751683"
## [1] "Standard deviations from mean: 2.58739155741733"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.89821029534966"
## [1] "Standard deviations from mean: 10.034880972535"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.24251646983205"
## [1] "Standard deviations from mean: 4.54464068539368"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.76753524229695"
## [1] "Standard deviations from mean: 13.1607758421138"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.79677233574885"
## [1] "Standard deviations from mean: 12.9741861694883"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.3639194869393"
## [1] "Standard deviations from mean: 3.01911792253967"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.49382403494025"
## [1] "Standard deviations from mean: 3.62526766796914"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.58106408175289"
## [1] "Standard deviations from mean: 3.35463193938967"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.85819896588427"
## [1] "Standard deviations from mean: 7.83543414462605"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31058239027344"
## [1] "Standard deviations from mean: 6.24780704095568"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.83070120165711"
## [1] "Standard deviations from mean: 14.6665482503819"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.68803526349134"
## [1] "Standard deviations from mean: 13.4787845822556"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.52609664045784"
## [1] "Standard deviations from mean: 4.19083836259959"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.29726131132156"
## [1] "Standard deviations from mean: 2.59075735818402"
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.26
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.87
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.53005309432026"
## [1] "Standard deviations from mean: 10.8629930557857"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.38
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47859459775011"
## [1] "Standard deviations from mean: 8.52171671270359"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.46840477664178"
## [1] "Standard deviations from mean: 9.45872288692324"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.24
## [1] ""
## [1] "endothelial-mural"
## [1] 0.6
## [1] ""
## [1] "interneurons"
## [1] 0.75
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 0.96
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.27875253978408"
## [1] "Standard deviations from mean: 2.21498627935773"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.16510157978425"
## [1] "Standard deviations from mean: 3.0757711352291"
## [1] ""
## [1] "microglia"
## [1] 0.06
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.61898283171582"
## [1] "Standard deviations from mean: 6.42824918717225"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.02
## [1] "Fold enrichment: 1.14738623521404"
## [1] "Standard deviations from mean: 2.43351059970434"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.71
## [1] ""
## [1] "interneurons"
## [1] 0.52
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.98
## [1] ""
## [1] "pyramidal SS"
## [1] 0.46
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.02
## [1] "Fold enrichment: 1.27013492529625"
## [1] "Standard deviations from mean: 2.53201931850234"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.36960966205838"
## [1] "Standard deviations from mean: 3.44243983266395"
## [1] ""
## [1] "interneurons"
## [1] 0.02
## [1] "Fold enrichment: 1.10602440785071"
## [1] "Standard deviations from mean: 1.89147014239509"
## [1] ""
## [1] "microglia"
## [1] 0.17
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.48260197129409"
## [1] "Standard deviations from mean: 4.94510810352423"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.15221985187446"
## [1] "Standard deviations from mean: 2.64064710896877"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.77
## [1] ""
## [1] "interneurons"
## [1] 0.95
## [1] ""
## [1] "microglia"
## [1] 0.81
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.95
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.6
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.16
## [1] ""
## [1] "endothelial-mural"
## [1] 0.52
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.33704865099281"
## [1] "Standard deviations from mean: 7.40613869834812"
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33537098255361"
## [1] "Standard deviations from mean: 3.31012553162413"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.38474998404645"
## [1] "Standard deviations from mean: 6.57224942160014"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28697578921463"
## [1] "Standard deviations from mean: 5.16826741412223"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.20956718731157"
## [1] "Standard deviations from mean: 2.01098547163833"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.29
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.68
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.15
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.79
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.38
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38636567606745"
## [1] "Standard deviations from mean: 7.27021212077539"
## [1] ""
## [1] "microglia"
## [1] 0.89
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.17538494431609"
## [1] "Standard deviations from mean: 1.84240286560029"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.42473904666186"
## [1] "Standard deviations from mean: 7.55860337421488"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30049635743458"
## [1] "Standard deviations from mean: 5.45693806525425"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.08
## [1] ""
## [1] "endothelial-mural"
## [1] 0.39
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.81
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.79
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.5
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.4
## [1] ""
## [1] "endothelial-mural"
## [1] 0.8
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.29144465882492"
## [1] "Standard deviations from mean: 6.22949262910354"
## [1] ""
## [1] "microglia"
## [1] 0.61
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.05
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.46970065256005"
## [1] "Standard deviations from mean: 7.94294569981396"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30390038215338"
## [1] "Standard deviations from mean: 6.27551010597199"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.65
## [1] ""
## [1] "interneurons"
## [1] 0.94
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.12
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.54
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31394892835994"
## [1] "Standard deviations from mean: 6.1173938461333"
## [1] ""
## [1] "microglia"
## [1] 0.48
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.13
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.44779456916031"
## [1] "Standard deviations from mean: 8.22710021064428"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.29153853239118"
## [1] "Standard deviations from mean: 5.10817757482449"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.32915673278872"
## [1] "Standard deviations from mean: 2.63273952641479"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.62
## [1] ""
## [1] "interneurons"
## [1] 0.99
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.92
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.72
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.11
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30287907402794"
## [1] "Standard deviations from mean: 6.40418355880191"
## [1] ""
## [1] "microglia"
## [1] 0.28
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37619185813792"
## [1] "Standard deviations from mean: 3.88909548278151"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.34925567557713"
## [1] "Standard deviations from mean: 6.16835467328472"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.20523887570248"
## [1] "Standard deviations from mean: 3.73047543861585"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.02
## [1] "Fold enrichment: 1.2345078048121"
## [1] "Standard deviations from mean: 2.00325776182715"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.39
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.91
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.87
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.42996639958263"
## [1] "Standard deviations from mean: 3.28211202146089"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.2803790310865"
## [1] "Standard deviations from mean: 2.40068779294221"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.25313038455265"
## [1] "Standard deviations from mean: 5.3863020655235"
## [1] ""
## [1] "microglia"
## [1] 0.18
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.44535137179669"
## [1] "Standard deviations from mean: 4.90458785086483"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.29068397841707"
## [1] "Standard deviations from mean: 4.93727519251717"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.16841017166969"
## [1] "Standard deviations from mean: 3.46700544574065"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.36696360234368"
## [1] "Standard deviations from mean: 2.93024584295815"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.41794909698978"
## [1] "Standard deviations from mean: 3.17077121039667"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.594983480188"
## [1] "Standard deviations from mean: 3.86912857865004"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 2.09605843990363"
## [1] "Standard deviations from mean: 11.9798183878005"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.55698264119325"
## [1] "Standard deviations from mean: 10.6010692407944"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.67180991235752"
## [1] "Standard deviations from mean: 11.3869500487921"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.66022158463122"
## [1] "Standard deviations from mean: 11.5082549610787"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.15
## [1] ""
## [1] "endothelial-mural"
## [1] 0.57
## [1] ""
## [1] "interneurons"
## [1] 0.93
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.96
## [1] ""
## [1] "pyramidal SS"
## [1] 0.91
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.19
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.40057184391778"
## [1] "Standard deviations from mean: 2.55313146551903"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.39996186724745"
## [1] "Standard deviations from mean: 2.31039974252778"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.06
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.04
## [1] "Fold enrichment: 1.21362653994441"
## [1] "Standard deviations from mean: 1.73082803952873"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.47
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.8
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.97
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.89
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.09
## [1] ""
## [1] "endothelial-mural"
## [1] 0.28
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.24
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.09
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.46
## [1] ""
## [1] "pyramidal SS"
## [1] 0.91
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.75
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.9
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.48
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.62
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.08
## [1] ""
## [1] "endothelial-mural"
## [1] 0.45
## [1] ""
## [1] "interneurons"
## [1] 0.73
## [1] ""
## [1] "microglia"
## [1] 0.05
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.86
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.11946937833408"
## [1] "Standard deviations from mean: 2.34446131585096"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.37
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.14
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0.99
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.4
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.9
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.23
## [1] ""
## [1] "endothelial-mural"
## [1] 0.05
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.08
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.72
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.9
## [1] ""
## [1] "pyramidal SS"
## [1] 0.92
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.17
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.37
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.95
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.94
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.94
## [1] ""
## [1] "endothelial-mural"
## [1] 0.84
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.32337264890325"
## [1] "Standard deviations from mean: 6.2116837511867"
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.27508029866987"
## [1] "Standard deviations from mean: 2.8786375666947"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.30343902889344"
## [1] "Standard deviations from mean: 4.93204992614422"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43055354120573"
## [1] "Standard deviations from mean: 8.31947139445578"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.14
## [1] ""
## [1] "endothelial-mural"
## [1] 0.24
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.76
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.26223151161346"
## [1] "Standard deviations from mean: 2.59043133720525"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.98
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.36870464124298"
## [1] "Standard deviations from mean: 3.34394567074562"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.16
## [1] ""
## [1] "interneurons"
## [1] 0.73
## [1] ""
## [1] "microglia"
## [1] 0.74
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.7
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.78
## [1] ""
## [1] "pyramidal SS"
## [1] 0.97
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.34115719802433"
## [1] "Standard deviations from mean: 2.90876896293013"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.34
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.58
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.31
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.3627028838159"
## [1] "Standard deviations from mean: 2.70302009986228"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31537620067647"
## [1] "Standard deviations from mean: 5.98986609686851"
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33364386151528"
## [1] "Standard deviations from mean: 4.02450240208077"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.01
## [1] "Fold enrichment: 1.13076377124095"
## [1] "Standard deviations from mean: 2.51095789921836"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28925562490391"
## [1] "Standard deviations from mean: 5.61668594748418"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4823749819384"
## [1] "Standard deviations from mean: 3.93833960224507"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.65
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.82
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39908119770173"
## [1] "Standard deviations from mean: 8.20790986568399"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.32
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47412414157004"
## [1] "Standard deviations from mean: 7.84349015856196"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.49911975638761"
## [1] "Standard deviations from mean: 9.56659473537242"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.2633411821024"
## [1] "Standard deviations from mean: 2.25059962967349"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.67
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 2.34438698015671"
## [1] "Standard deviations from mean: 10.2850431979544"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 2.20934908576778"
## [1] "Standard deviations from mean: 10.4605140577291"
## [1] ""
## [1] "interneurons"
## [1] 0.3
## [1] ""
## [1] "microglia"
## [1] 0.04
## [1] "Fold enrichment: 1.41271134947188"
## [1] "Standard deviations from mean: 2.3107487415948"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.46441253180761"
## [1] "Standard deviations from mean: 4.68129096454793"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.97
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.25848007054243"
## [1] "Standard deviations from mean: 2.02384462845744"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.9
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.95
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.9
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30008675377373"
## [1] "Standard deviations from mean: 5.42031403715552"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.06
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28613948352802"
## [1] "Standard deviations from mean: 4.92855167221698"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28062017245432"
## [1] "Standard deviations from mean: 5.31129428111827"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.11447815932378"
## [1] "Standard deviations from mean: 2.09159270771025"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.53
## [1] ""
## [1] "endothelial-mural"
## [1] 0.1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.2567000331111"
## [1] "Standard deviations from mean: 5.823761815847"
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.14249586110947"
## [1] "Standard deviations from mean: 1.67251676999416"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.65
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41971612339772"
## [1] "Standard deviations from mean: 3.51058535564215"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.35
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.89
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.4150074517071"
## [1] "Standard deviations from mean: 8.5208885544443"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.26
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.41655204777831"
## [1] "Standard deviations from mean: 7.16171857119791"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.4073703625943"
## [1] "Standard deviations from mean: 7.96075859275961"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.2578688324965"
## [1] "Standard deviations from mean: 2.52283635811912"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.33
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.09
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.34124327189425"
## [1] "Standard deviations from mean: 3.39495369391125"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43538308689614"
## [1] "Standard deviations from mean: 8.39895492264893"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.68
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.45138433967613"
## [1] "Standard deviations from mean: 7.92033419798832"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.48043339961085"
## [1] "Standard deviations from mean: 9.22131641168356"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.38763056109799"
## [1] "Standard deviations from mean: 3.67978337177896"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.31
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.45
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41037174922249"
## [1] "Standard deviations from mean: 3.90731252645131"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.91497462930138"
## [1] "Standard deviations from mean: 7.03185915983936"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.28375074944581"
## [1] "Standard deviations from mean: 5.59750081235203"
## [1] ""
## [1] "microglia"
## [1] 0.33
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.09
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.44
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.18402102170687"
## [1] "Standard deviations from mean: 3.18818693941427"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.30998778171148"
## [1] "Standard deviations from mean: 2.65563038799159"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.68
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.14
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30643621630414"
## [1] "Standard deviations from mean: 6.28323424726205"
## [1] ""
## [1] "microglia"
## [1] 0.75
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.42
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.08
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.22096190760641"
## [1] "Standard deviations from mean: 4.20758763015106"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4233602033347"
## [1] "Standard deviations from mean: 3.34655526057046"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.4
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.53
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.98
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.37142565889519"
## [1] "Standard deviations from mean: 7.61821600874018"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.13
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.5281425836805"
## [1] "Standard deviations from mean: 9.34630109808363"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.60026886047134"
## [1] "Standard deviations from mean: 11.5171999874655"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.40351045744542"
## [1] "Standard deviations from mean: 3.02864627650639"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.56
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43321983170287"
## [1] "Standard deviations from mean: 8.98127431405562"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.02
## [1] "Fold enrichment: 1.18443743673693"
## [1] "Standard deviations from mean: 2.09424180798725"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.25079983630804"
## [1] "Standard deviations from mean: 4.1592618720545"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.33007645619501"
## [1] "Standard deviations from mean: 6.20145618524968"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.43902964300419"
## [1] "Standard deviations from mean: 3.51203374583504"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.81
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.8
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.97
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.3527570522622"
## [1] "Standard deviations from mean: 6.4564748117626"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.21787848660516"
## [1] "Standard deviations from mean: 2.40407228399115"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.50680502603419"
## [1] "Standard deviations from mean: 9.74149985433321"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.60480848128911"
## [1] "Standard deviations from mean: 12.3279086990675"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.30165031481362"
## [1] "Standard deviations from mean: 2.91894882639394"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.27
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.91
## [1] ""
## [1] "endothelial-mural"
## [1] 0.74
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43562743942985"
## [1] "Standard deviations from mean: 8.29866369629902"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.19
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33514251994228"
## [1] "Standard deviations from mean: 5.80942661119468"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30381838199839"
## [1] "Standard deviations from mean: 5.68950336488475"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.49086306422457"
## [1] "Standard deviations from mean: 4.28604372228488"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.42
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.8
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.88
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.34612727454254"
## [1] "Standard deviations from mean: 6.59714653108469"
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.11
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.27915196135261"
## [1] "Standard deviations from mean: 5.05761409564878"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.38152487175855"
## [1] "Standard deviations from mean: 7.27000855974018"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41975936273514"
## [1] "Standard deviations from mean: 3.98120346291382"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.25
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39864057422312"
## [1] "Standard deviations from mean: 8.61198427654143"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.22448644954557"
## [1] "Standard deviations from mean: 2.44343527043834"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.34649957554668"
## [1] "Standard deviations from mean: 6.85332191081762"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.35156421103183"
## [1] "Standard deviations from mean: 6.30664236799328"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.92
## [1] ""
## [1] "endothelial-mural"
## [1] 0.76
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.35758518927623"
## [1] "Standard deviations from mean: 7.00350624455461"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.20246503521053"
## [1] "Standard deviations from mean: 1.8767100555157"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.24454338653484"
## [1] "Standard deviations from mean: 4.05436476036237"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30299518195013"
## [1] "Standard deviations from mean: 5.11873168238985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.44621033351169"
## [1] "Standard deviations from mean: 3.52925041663019"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.5
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.92
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30524616238526"
## [1] "Standard deviations from mean: 5.57754542390505"
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.19110345839714"
## [1] "Standard deviations from mean: 2.36978916297784"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33112906211396"
## [1] "Standard deviations from mean: 5.80698816967745"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43015870269901"
## [1] "Standard deviations from mean: 8.12654218634643"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.37710612615752"
## [1] "Standard deviations from mean: 3.25072805984142"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.44
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.87
## [1] ""
## [1] "endothelial-mural"
## [1] 0.66
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44891705400785"
## [1] "Standard deviations from mean: 8.76072691559011"
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.05
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32038508387217"
## [1] "Standard deviations from mean: 5.83248576316005"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.37374233436999"
## [1] "Standard deviations from mean: 6.34668263502661"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.44022400912604"
## [1] "Standard deviations from mean: 3.55072306146624"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.48
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.85
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.8
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.98
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.40709698005332"
## [1] "Standard deviations from mean: 8.09206400945743"
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.26
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47119020717666"
## [1] "Standard deviations from mean: 8.57557975114125"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.58380281696041"
## [1] "Standard deviations from mean: 10.5441974474243"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.21903708533958"
## [1] "Standard deviations from mean: 1.87730072497057"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.42
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.67
## [1] ""
## [1] "endothelial-mural"
## [1] 0.31
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.27071434528459"
## [1] "Standard deviations from mean: 5.2285046862428"
## [1] ""
## [1] "microglia"
## [1] 0.45
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.02
## [1] "Fold enrichment: 1.19564719049588"
## [1] "Standard deviations from mean: 2.17454988211899"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.14900771945043"
## [1] "Standard deviations from mean: 2.71585040616355"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24214820357332"
## [1] "Standard deviations from mean: 4.08714486088249"
## [1] ""

Subset regions enriched in certain cell types

cell.type1 <-"astrocytes-ependymal"
cell.type2 <-"oligodendrocytes"
cell.type3 <-"interneurons"

enrichment.summary <-data.frame()
for(region in region_list){
  # 1st cell type
  if(subset(tt_results[[region]]$joint_results, CellType==cell.type1 &
            Direction=="Up")$p<=0.05){enriched1="Yes"} else{enriched1="No"}
  # 2nd cell type
    if(subset(tt_results[[region]]$joint_results, CellType==cell.type2 &
            Direction=="Up")$p<=0.05){enriched2="Yes"} else{enriched2="No"}
  # 3rd cell type
    if(subset(tt_results[[region]]$joint_results, CellType==cell.type3 &
            Direction=="Up")$p<=0.05){enriched3="Yes"} else{enriched3="No"}
  # Summarise
  enrichment.summary <-rbind(enrichment.summary, 
            data.frame(Region=region, Cell.Type=cell.type1, Enriched=enriched1), 
            data.frame(Region=region, Cell.Type=cell.type2, Enriched=enriched2),
            data.frame(Region=region, Cell.Type=cell.type3, Enriched=enriched3))
}

# Get only significantly enriched regions
enrich.summ <-subset(enrichment.summary, Enriched=="Yes")
pander(enrich.summ[order(enrich.summ$Region),], style='simple',justify='left')

	Region	Cell.Type	Enriched
10	rCG6	astrocytes-ependymal	Yes
59	ic	oligodendrocytes	Yes
61	IsCj	astrocytes-ependymal	Yes
70	GPe	astrocytes-ependymal	Yes
71	GPe	oligodendrocytes	Yes
73	GPi	astrocytes-ependymal	Yes
74	GPi	oligodendrocytes	Yes
76	V1-1	astrocytes-ependymal	Yes
85	V1-4A	astrocytes-ependymal	Yes
88	V1-4B	astrocytes-ependymal	Yes
94	V14Cb	astrocytes-ependymal	Yes
97	V1-5	astrocytes-ependymal	Yes
100	V1-6	astrocytes-ependymal	Yes
103	V1wm	astrocytes-ependymal	Yes
104	V1wm	oligodendrocytes	Yes
109	V2-3	astrocytes-ependymal	Yes
118	NAC	astrocytes-ependymal	Yes
122	V2-6	oligodendrocytes	Yes
124	CA2py	astrocytes-ependymal	Yes
127	DGpf	astrocytes-ependymal	Yes
130	DGgr	astrocytes-ependymal	Yes
133	S	astrocytes-ependymal	Yes
139	CA1ra	astrocytes-ependymal	Yes
142	CA1or	astrocytes-ependymal	Yes
143	CA1or	oligodendrocytes	Yes
145	DGsg	astrocytes-ependymal	Yes
148	CA3py	astrocytes-ependymal	Yes
151	AA	astrocytes-ependymal	Yes
154	PL	astrocytes-ependymal	Yes
157	Me	astrocytes-ependymal	Yes
160	L	astrocytes-ependymal	Yes
163	APir	astrocytes-ependymal	Yes
166	rPAC	astrocytes-ependymal	Yes
172	AHA	astrocytes-ependymal	Yes
175	B	astrocytes-ependymal	Yes
178	CE	astrocytes-ependymal	Yes
181	CA4	astrocytes-ependymal	Yes

Signatures_of_Neurogensis

Brian Schilder

10/1/2017

Import Datasets

ABA data: Mouse

ABA data: Adult Human Microarray

ABA data: Developmental Macaque Microarray

Macaque Preprocessing

Convert to eset

Import Miller et al. 2013 gene sets

Identify Macaque SGZ-enriched genes

Candidate Genes

[Approach 1: Unsupervised Clustering]

PCA: All Genes

Unsupervised Clustering

All Genes

Gene Subset: Mouse SGZ-enriched (Table S1)

Gene Subset: ‘Macaque Tan Module’ genes only

[Approach 2: Compare Expression Profiles]

Differential Expression

All Genes

Mouse SGZ-enriched genes (Table S1)

Macaque Tan module genes (Table S4)

[Approach 3: Compare Network Modules]

WGCNA: Macaque Microarray (SGZ/GCL samples only, all ages)

Rough ‘% overlap with genes in Tan module’ metric

WGCNA: Macaque Microarray (all regions, all genes, all ages)

Rough ‘% overlap with genes in Tan module’ metric

Network Preservation Analysis

Plot network preservation scores

[Approach 4: Deconvolution]

Format scRNAseq reference database

‘Mouse SGZ-enriched’ genes (Table S1)

Setup data

Sub-cell type level

Cell type level

‘Macaque tan module’ genes (Table S4)

Setup data

Sub-cell type level

Cell type level

Iteratively run EWCE on each region’s transcriptome

Run DGE: Each region vs. all other regions

Run EWCE for each region

Subset regions enriched in certain cell types